Patent Publication Number: US-8543520-B2

Title: Navigation system with single pass clustering based template generation mechanism and method of operation thereof

Description:
TECHNICAL FIELD 
     The present invention relates generally to a navigation system, and more particularly to a system for single pass clustering based template generation mechanism. 
     BACKGROUND ART 
     Modern portable consumer and industrial electronics, especially client devices such as navigation systems, cellular phones, portable digital assistants, and combination devices, are providing increasing levels of functionality to support modern life including location-based information services. Research and development in the existing technologies can take a myriad of different directions. 
     As users become more empowered with the growth of mobile location based service devices, new and old paradigms begin to take advantage of this new device space. There are many technological solutions to take advantage of this new device location opportunity. One existing approach is to use location information to provide navigation services such as a global positioning system (GPS) for a car or on a mobile device such as a cell phone, portable navigation device (PND) or a personal digital assistant (PDA). 
     Location based services allow users to create, transfer, store, and/or consume information in order for users to create, transfer, store, and consume in the “real world”. One such use of location based services is to efficiently transfer or route users to the desired destination or service. 
     Navigation systems and location based services enabled systems have been incorporated in automobiles, notebooks, handheld devices, and other portable products. Today, these systems aid users by incorporating available, real-time relevant information, such as maps, directions, local businesses, or other points of interest (POI). The real-time information provides invaluable relevant information. However, a failure to extract all the fields and align the fields from one document to another document continues to be a paramount concern for the consumer. 
     Thus, a need still remains for a navigation system with single pass clustering based template generation mechanism to generate a document that can extract and align all the fields from a source document to a revised version of the document. In view of the ever-increasing commercial competitive pressures, along with growing consumer expectations and the diminishing opportunities for meaningful product differentiation in the marketplace, it is increasingly critical that answers be found to these problems. Additionally, the need to reduce costs, improve efficiencies and performance, and meet competitive pressures adds an even greater urgency to the critical necessity for finding answers to these problems. 
     Solutions to these problems have been long sought but prior developments have not taught or suggested any solutions and, thus, solutions to these problems have long eluded those skilled in the art. 
     DISCLOSURE OF THE INVENTION 
     The present invention provides a method of operation of a navigation system including: extracting navigation-related web documents having a point of interest; generating formatting sequences of the navigation-related web documents; selecting a user-defined percentile representing reciprocal fraction of an expected number of clusters; calculating a threshold value for a first cluster with the threshold value to be equal to the user-defined percentile of a first normalized distribution of sample comparison values between the first cluster and formatting sequence samples from the formatting sequences, the first cluster is from the clusters; computing an associated comparison value between a first formatting sequence from the formatting sequences and the first cluster; grouping the first formatting sequence with the first cluster when the associated comparison value exceeds the threshold value for the first cluster; and generating a travel route for the point of interest related to the first cluster for displaying on a device. 
     The present invention provides a navigation system, including: an extraction module, for extracting navigation-related web documents having a point of interest; a feature generation module, coupled to the extraction module, for generating formatting sequences of the navigation-related web documents; a systems setting module for selecting a user-defined percentile representing reciprocal fraction of an expected number of clusters; a threshold generator module, coupled to the systems setting module, the document processing module, and the clusters module, for calculating a threshold value for a first cluster with the threshold value to be equal to the user-defined percentile of a first normalized distribution of sample comparison values between the first cluster and formatting sequence samples from the formatting sequences, the first cluster is from the clusters; a comparison module, coupled with the feature generation module, and the threshold generator module, for computing an associated comparison value for computing an associated comparison value between a first formatting sequence from the formatting sequences and the first cluster; a grouping module, coupled with the comparison module, the document processing module, and the threshold generator module, for grouping the first formatting sequence with the first cluster when the associated comparison value exceeds the threshold value for the first cluster; and a route generation module, coupled with the grouping module, for generating a travel route for the point of interest related to the first cluster for displaying on a device. 
     Certain embodiments of the invention have other steps or elements in addition to or in place of those mentioned above. The steps or elements will become apparent to those skilled in the art from a reading of the following detailed description when taken with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a navigation system with single pass clustering based template generation mechanism in an embodiment of the present invention. 
         FIG. 2  is an example of an original document for a point of interest. 
         FIG. 3  is an example of templates of the documents of  FIG. 2 , including a first template and a second template. 
         FIG. 4  is an example of clusters based on the templates of  FIG. 3  used in the formations of the documents of  FIG. 2 . 
         FIG. 5  is an example of an extracted document failing to include some of the fields for the review from the original document. 
         FIG. 6  is an example of the navigation system extracting all of the fields for the review from the original document to generate a point-of-interest page. 
         FIG. 7  is an example of the navigation system generating a travel guide for the point of interest of  FIG. 2  from the clusters of the documents. 
         FIG. 8  is a display example of the travel guide for the navigation system. 
         FIG. 9  is a flowchart of an example of a single pass clustering method of the navigation system for generating the clusters of  FIG. 4 . 
         FIG. 10  is a first normalized distribution of sample comparison values calculated from matching pairs between formatting sequence samples and the first cluster of  FIG. 4 . 
         FIG. 11  is a bar graph of the number of the clusters of  FIG. 4  generated across examples of the navigation system of  FIG. 1 . 
         FIG. 12  is a bar graph of multiple numbers of the threshold value calculated for nine different clusters generated across five different examples of the navigation system. 
         FIG. 13  is an exemplary block diagram of the navigation system. 
         FIG. 14  is a control flow of the navigation system. 
         FIG. 15  is a control flow of the threshold generator module. 
         FIG. 16  is a flow chart of a method of operation of the navigation system in a further embodiment of the present invention. 
     
    
    
     BEST MODE FOR CARRYING OUT THE INVENTION 
     The following embodiments are described in sufficient detail to enable those skilled in the art to make and use the invention. It is to be understood that other embodiments would be evident based on the present disclosure, and that system, process, or mechanical changes may be made without departing from the scope of the present invention. 
     In the following description, numerous specific details are given to provide a thorough understanding of the invention. However, it will be apparent that the invention may be practiced without these specific details. In order to avoid obscuring the present invention, some well-known circuits, system configurations, and process steps are not disclosed in detail. 
     The drawings showing embodiments of the system are semi-diagrammatic and not to scale and, particularly, some of the dimensions are for the clarity of presentation and are shown exaggerated in the drawing FIGs. Similarly, although the views in the drawings for ease of description generally show similar orientations, this depiction in the FIGs. is arbitrary for the most part. Generally, the invention can be operated in any orientation. The embodiments have been numbered first embodiment, second embodiment, etc. as a matter of descriptive convenience and are not intended to have any other significance or provide limitations for the present invention. 
     One skilled in the art would appreciate that the format with which navigation information is expressed is not critical to some embodiments of the invention. For example, in some embodiments, navigation information is presented in the format of (X, Y), where X and Y are two ordinates that define the geographic location, i.e., a position of a user. 
     In an alternative embodiment, navigation information is presented by longitude and latitude related information. In a further embodiment of the present invention, the navigation information also includes a velocity element including a speed component and a heading component. 
     The term “navigation-related information” or “relevant information” referred to herein includes the navigation information described as well as information relating to points of interest to the user, such as local business, hours of businesses, types of businesses, advertised specials, traffic information, maps, local events, and nearby community or personal information. 
     The term “module” referred to herein can include software, hardware, or a combination thereof. For example, the software can be machine code, firmware, embedded code, and application software. Also for example, the hardware can be circuitry, processor, computer, integrated circuit, integrated circuit cores, a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     Referring now to  FIG. 1 , therein is shown a navigation system  100  with single pass clustering based template generation mechanism in an embodiment of the present invention. The navigation system  100  includes a first device  102 , such as a client or a server, connected to a second device  106 , such as a client or server, with a communication path  104 , such as a wireless or wired network. 
     For example, the first device  102  can be of any of a variety of mobile devices, such as a cellular phone, personal digital assistant, a notebook computer, automotive telematic navigation system, or other multi-functional mobile communication or entertainment device. The first device  102  can be a standalone device, or can be incorporated with a vehicle, for example a car, truck, bus, or train. The first device  102  can couple to the communication path  104  to communicate with the second device  106 . 
     For illustrative purposes, the navigation system  100  is described with the first device  102  as a mobile computing device, although it is understood that the first device  102  can be different types of computing devices. For example, the first device  102  can also be a non-mobile computing device, such as a server, a server farm, or a desktop computer. 
     The second device  106  can be any of a variety of centralized or decentralized computing devices. For example, the second device  106  can be a computer, grid computing resources, a virtualized computer resource, cloud computing resource, routers, switches, peer-to-peer distributed computing devices, or a combination thereof. 
     The second device  106  can be centralized in a single computer room, distributed across different rooms, distributed across different geographical locations, embedded within a telecommunications network. The second device  106  can have a means for coupling with the communication path  104  to communicate with the first device  102 . The second device  106  can also be a client type device as described for the first device  102 . 
     In another example, the first device  102  can be a particularized machine, such as a mainframe, a server, a cluster server, rack mounted server, or a blade server, or as more specific examples, an IBM System z10™ Business Class mainframe or a HP ProLiant ML™ server. Yet another example, the second device  106  can be a particularized machine, such as a portable computing device, a thin client, a notebook, a netbook, a smartphone, personal digital assistant, or a cellular phone, and as specific examples, an Apple iPhone™, Palm Centro™, or Moto Q Global™. 
     For illustrative purposes, the navigation system  100  is described with the second device  106  as a non-mobile computing device, although it is understood that the second device  106  can be different types of computing devices. For example, the second device  106  can also be a mobile computing device, such as notebook computer, another client device, or a different type of client device. The second device  106  can be a standalone device, or can be incorporated with a vehicle, for example a car, truck, bus, or train. 
     Also for illustrative purposes, the navigation system  100  is shown with the second device  106  and the first device  102  as end points of the communication path  104 , although it is understood that the navigation system  100  can have a different partition between the first device  102 , the second device  106 , and the communication path  104 . For example, the first device  102 , the second device  106 , or a combination thereof can also function as part of the communication path  104 . 
     The communication path  104  can be a variety of networks. For example, the communication path  104  can include wireless communication, wired communication, optical, ultrasonic, or the combination thereof. Satellite communication, cellular communication, Bluetooth, Infrared Data Association standard (IrDA), wireless fidelity (WiFi), and worldwide interoperability for microwave access (WiMAX) are examples of wireless communication that can be included in the communication path  104 . Ethernet, digital subscriber line (DSL), fiber to the home (FTTH), and plain old telephone service (POTS) are examples of wired communication that can be included in the communication path  104 . 
     Further, the communication path  104  can traverse a number of network topologies and distances. For example, the communication path  104  can include direct connection, personal area network (PAN), local area network (LAN), metropolitan area network (MAN), wide area network (WAN) or any combination thereof. 
     Referring now to  FIG. 2 , therein is shown an example of an original document  202  for a point of interest  206 . The original document  202  can be one of documents  201 . The documents  201  are digital files containing real world information on storage devices, such as the first device  102  of  FIG. 1 , and the second device  106  of  FIG. 1 . The documents  201  can include navigation-related web documents  203 . 
     The navigation-related web documents  203  are defined as web documents containing navigation-related information regarding point of interest  206 . The original document  202  is defined as one of the navigation-related web documents  203  accessed by the navigation system  100  for a review  204  of the point of interest  206 . The point of interest  206  is defined as the physical location where the user of the navigation system  100  is interested to reach at the end of the travel. The review  204  is defined as a critique, a comment, or a combination thereof regarding the point of interest  206  and all related information about the critique, comment, or the combination. 
     For example, the point of interest  206  can be New York City. The review  204  can be comments of New York City made by travelers who had visited New York City, and other information regarding the comments, such as when, where, from who, why, and how the comments are submitted. The original document  202  can represent a travel document, such as www.tripadvisor.com™. The original document  202  can include the review  204  of New York City by travelers who had traveled to New York City. The original document  202  can also include multiple numbers of the review  204 . In this example, the original document  202  can include two types of the review  204  with a travel review title  208  of “NY FAVE” and “LOVE NY.” 
     The review  204  can present one of fields  210 , such as the travel review title  208 , a travel review time  212 , a travel review rating  214 , a travel review content  216 , or a combination thereof. The fields  210  are defined as particular areas on the review  204  that provide information to the viewer. For example, one of the fields  210  can represent the travel review title  208 . 
     The travel review title  208  is defined as a heading that specifies the title of the review  204 . For example, the first of the travel review title  208  of the review  204  can represent “NY FAVE” and the second of the travel review title  208  of the review  204  can represent “LOVE NY.” 
     The travel review time  212  is defined as the time when the submission of the review  204  was made to the original document  202 . For example, the travel review time  212  can represent “Sep. 13, 2010.” 
     The travel review rating  214  is defined as an appraisal value submitted as part of the review  204  to the original document  202  by the travelers to rate the point of interest  206 . For example, one traveler can rate three stars as the travel review rating  214  and another traveler can rate two stars as the travel review rating  214  for New York City at the original document  202 . 
     The travel review content  216  is defined as a summary of critique, comment, or a combination thereof submitted as part of the review  204  to the original document  202 . For example, one traveler can write, “Can&#39;t get enough of great food!!” as the travel review content  216  for visiting New York City. 
     Referring now to  FIG. 3 , therein is shown an example of templates  302  of the documents  201  of  FIG. 2 , including a first template  304  and a second template  306 . The templates  302  are defined as pre-defined formatting arrangements for insertion or extraction of contents, where the contents are presented in the same formatted arrangement. 
     The documents  201  can be interpreted and displayed by their corresponding formatting sequences  303 . The formatting sequences  303  are sets of code describing how documents should be presented and displayed, such as by standard protocols. For example, the standard protocols can represent Hyper Text Markup Language (HTML), Extensible Markup Language (XML), or a combination thereof. The first template  304  and the second template  306  can use pre-defined formatting sequences for formatting and displaying documents. Each template can have designated areas to display specific types of contents. Each designated area can correspond to an expression of the standard protocols, which can define the formatting arrangement for that designated area. 
     Extraction of navigation-related information can require a substantially complete understanding of the formatting of a document. For example, because the first template  304  includes an arrangement for a location for a review content field differently from another location for the review content field in the second template  306 , the extraction rules of navigation-related information from a document generated via the first template  304  must be different from the extraction rules of navigation-related information from a document generated via the second template  306 . 
     Referring now to  FIG. 4 , therein is shown an example of clusters  402  based on the templates  302  of  FIG. 3  used in the formations of the documents  201  of  FIG. 2 . The clusters  402  are defined as sets of documents grouped together because of some shared similarity in each of the sets, such as use of the same template in generating the documents in each of the clusters  402 . 
     The clusters  402  can include a first cluster  404 . The first cluster  404  is an instance of the clusters  402 . The first cluster  404  can include a first document  406 , a second document  408 , and a third document  410 . The first document  406 , the second document  408 , and the third document  410  are each instances of the documents  201 , and each can be the original document  202  of  FIG. 2  containing the review  204  of  FIG. 2 . The first document  406 , the second document  408 , and the third document  410  share the first template  304  of  FIG. 3  that is the same, for example. 
     A second cluster  412  can be an instance of the clusters  402 . The second cluster  412  can include a fourth document  414  and a fifth document  416 . The fourth document  414  and the fifth document  416  can share the same second template  306  of  FIG. 3 . The first, second, third, fourth, and fifth documents can all be instances of the documents  201 . 
     The clusters  402  can have centers  422 . The centers  422  are instances of the formatting sequences  303 . Each of the centers  422  of the clusters  402  is a formatting sequence used for comparison between another formatting sequence and each of the clusters. Each of the centers  422  can be chosen by a pre-defined rule, such as the first formatting sequence or first document to be grouped with each of the clusters  402 . 
     The first cluster  404  can have a first cluster center  418 . The first cluster center  418  is an instance of the centers  422  for the first cluster  404 . The second cluster  412  can have a second cluster center  420 . The second cluster center  420  is an instance of the centers  422 . For example, the formatting sequence of the second document  408  can be the first cluster center  418  for the first cluster  404 . The formatting sequence for the fifth document  416  can be the second cluster center  420 . 
     Referring now to  FIG. 5 , therein is shown an example of an extracted document  502  failing to include some of the fields  210  for the review  204  from the original document  202 . The extracted document  502  is defined as a document with an extraction of the fields  210  of the review  204  from the original document  202 . 
     For example, the original document  202  is illustrated in  FIG. 2 . A system without single pass clustering based template generation mechanism can generate the extracted document  502 . Without the benefit of utilizing the present invention, the extracted document  502  can have some of the fields  210  missing, as the system without single pass clustering based template generation mechanism can fail to extract all of the fields  210  from the review  204 . Additionally, the system without single pass clustering based template generation mechanism can fail to extract at least one of the fields  210  that is for the review  204 . 
     For example, the first submission of the review  204  for the extracted document  502  can include the travel review title  208 , but fail to include the travel review time  212  for the first submission of the review  204 . Instead, the extracted document  502  can include the travel review time  212  for the second submission of the review  204  for the first submission of the review  204 . As further example, the second of the review  204  can fail to include the travel review content  216  and the travel review time  212 . 
     Referring now to  FIG. 6 , therein is shown an example of the navigation system  100  extracting all of the fields  210  for the review  204  from the original document  202  to generate a point-of-interest page  602 . The point-of-interest page  602  is defined as a document that the navigation system  100  had generated by extracting all of the fields  210  of the review  204  from the original document  202 . For example, the original document  202  is illustrated in  FIG. 2 . The navigation system  100  can generate the point-of-interest page  602  by compiling all of the fields  210  presented in the review  204  of the original document  202 . The point-of-interest page  602  includes all of the fields  210  presented in the review  204  because the extraction method employed by the navigation system  100  can in fact be error-free unlike the example in  FIG. 5 . 
     Referring now to  FIG. 7 , therein is shown an example of the navigation system  100  generating a travel guide  702  for the point of interest  206  of  FIG. 2  from the clusters  402  of the documents  201 . The travel guide  702  is defined as a display of point of interest related information extracted from the documents  201 . 
     The navigation system  100  can extract the documents  201  from the World Wide Web or other sources. The navigation system  100  can then extract the formatting sequences  303  from the documents  201  including a first formatting sequence  710 . Extraction of the formatting sequences  303  is a process involving reading the digital information contained from the sources of the documents  201 , and converting them to a language of standard protocols that describes the formats of the documents  201 . The first formatting sequence  710  is one instance of the formatting sequences  303 . The navigation system  100  can then group the formatting sequences  303  into the clusters  402 . The navigation system  100  can then generate the templates  302  from the clusters  402 . 
     Each of the templates  302  can define or transform into a set of parsing rules on standard or pre-defined protocols that applies to a group of documents to extract information or navigation-related information from therein, such as the first template  304 . The navigation system  100  can generate the travel guide  702  for the point of interest  206 . Display of the travel guide  702  is further explained in  FIG. 8 . 
     Referring now to  FIG. 8 , therein is shown a display example of the travel guide  702  for the navigation system  100 . The travel guide  702  can include the point-of-interest page  602  generated by the navigation system  100 . 
     An entry  804  is defined as a selection into the first device  102  or the second device  106  of  FIG. 2  made by the user, the navigation system  100 , or a combination thereof from the point-of-interest page  602  to reach the point of interest  206 . For example, the entry  804  can represent a manual entry, an oral command, or a combination thereof. 
     A travel route  806  is defined as a path that a vehicle or a person with the navigation system  100  can take to reach from a current location  808  to the point of interest  206 . The current location  808  is defined as the current physical location of the navigation system  100 . For example, the travel route  806  can represent a path from the current location  808  representing Edgewater, N.J. to the point of interest  206  representing New York City. The travel route  806  can be included in the travel guide  702 . 
     Referring now to  FIG. 9 , therein is shown a flowchart of an example of a single pass clustering method  902  of the navigation system  100  for generating the clusters  402  of  FIG. 4 . A clustering method can categorize the documents  201  of  FIG. 2  based on the documents&#39; similarities or differences. The single pass clustering method  902  is a type of clustering method where a particular document is grouped or not grouped to a particular cluster by evaluating whether an associated comparison value  903  between the particular document and the particular cluster is greater than a threshold value  904 . 
     The threshold value  904  is a comparison value used to determine whether or not to group a formatting sequence or a document into a cluster. Each of comparison values  905  is defined as a measure of how similar or how different a document is from another document or cluster. The comparison values  905  can be implemented by a measure of distance between pairs of observations regarding the formatting sequences  303  of  FIG. 3 , such as the Euclidean distance or the Mahalanobis distance. The comparison values  905  can also be implemented by measuring the alignment match between pairs of the formatting sequences  303 . 
     It has been discovered that the single pass clustering method  902  has the advantage of being adaptable to different clustering applications. The single pass clustering method  902  can use any valid measure of document comparison to evaluate how similar documents are compared to each other. Documents can be compared without converting documents into numeric values. The single pass clustering method  902 , thereby, is highly adaptable to different cluster applications for different types of documents. 
     It has also been discovered that the single pass clustering method  902  has the advantage of being scalable to cluster a larger number of formatting sequences. The clustering method has linear complexity and can take the formatting sequences  303  sequentially. A linear complexity method is one where time taken to group the formatting sequences  303  is proportional to the total number of formatting sequences processed. The single pass clustering method  902  is therefore capable of clustering a larger number of the formatting sequences  303  without having the computation time grow polynomially or exponentially. 
     As a specific example, the single pass clustering method  902  can include: initializing the clusters  402  by creating a new initial cluster c 0 , and picking an ungrouped one of the formatting sequences  303  as the center of the new initial cluster, then compute an initial cluster threshold for the new initial cluster in a block  906 ; for all clusters and a further one of the formatting sequences  303  d i , computing the comparison values  905  in a block  908 ; checking whether the associated comparison value  903  between the further formatting sequence and a further one of the clusters  402  is greater than the threshold value  904  of the further cluster in a block  910 ; grouping the further formatting sequence into the further cluster if the block  910  responds “yes” in a block  912 ; proceeding again to the block  908  if all of the formatting sequences  303  have not been processed in a block  914 ; creating a new singleton cluster if the block  910  responds “no”, while picking a next formatting sequence as the new singleton cluster&#39;s center in a block  916 ; and terminating the method if all formatting sequences are processed in a block  918 . 
     Under the single pass clustering method  902 , each of the clusters  402 , denoted c k , will have its own threshold θ k  for its scope definition. The clusters  402  c k  have the centers  422  of  FIG. 4 . For example, the associated comparison value  903  between the first formatting sequence  710  of  FIG. 7  and the first cluster  404  can be same as the comparison value between the first formatting sequence  710  and the first cluster center  418  of  FIG. 4 . The associated comparison value  903  between a formatting sequence d i  and the cluster c k &#39;s center is formulated as:
 
 x   k =similarity( d   i ,c k ), k≦M,i≦N.   Equation 1
 
     If x k &gt;θ k , then the formatting sequence is deemed as close enough to the cluster c k  and it could be merged into the cluster. If x k ≦θ k , then the formatting sequence will be isolated as a new singleton cluster whose center is d i . 
     It has been discovered that the present invention provides the navigation system  100  to extract point-of-interest related information accurately. The navigation system  100  can estimate the threshold value  904  for the single pass clustering method  902  dynamically, and removing the need for setting the threshold value  904  manually. This new dynamic thresholding strategy of finding an appropriate threshold value to determine whether or not to put a formatting sequence into a cluster provides stability to the single pass clustering method  902  even when the nature of documents analyzed changes. Accuracy generally degrades for a point of interest information extraction method when nature of documents changes, but not if the extraction method adapts dynamically. The navigation system  100  thereby provides improved accuracy in point-of-interest related information extraction by estimating the threshold value  904  dynamically. 
     Referring now to  FIG. 10 , therein is shown a first normalized distribution  1002  of sample comparison values  1004  calculated from matching pairs  1006  between formatting sequence samples  1008  and the first cluster  404  of  FIG. 4 . The formatting sequence samples  1008  are a random sampling of the formatting sequences  303  of  FIG. 3 , used to approximate the actual mean and actual standard deviation of the comparison values  905  of  FIG. 9  between the first cluster  404  and all of the formatting sequences  303 . The matching pairs  1006  are defined as associations between each of the formatting sequence samples  1008  and the first cluster  404 , pending comparison. The sample comparison values  1004  are defined as the comparison values between the matching pairs  1006 . 
     The navigation system  100  creates the formatting sequence samples  1008  by sampling the formatting sequences  303 . Each of the formatting sequence samples  1008  are matched to the first cluster  404 , creating the matching pairs  1006 . Then the sample comparison values  1004  are calculated for each of the matching pairs  1006 . The sample comparison values  1004  are used to calculate the first normalized distribution  1002 . 
     The first normalized distribution  1002  is a normal approximation of the sample comparison values  1004 , useful to determine the threshold value  904  of  FIG. 9  that can be used in the single pass clustering method  902  of  FIG. 9 . The first normalized distribution  1002  includes a sample mean  1010 , and a sample standard deviation  1012 . The sample mean  1010  is a statistical average of the sample comparison values  1004 . The sample standard deviation  1012  is a measure of variability of the sample comparison values  1004 . Equation 2 and Equation 3 describe how to calculate the sample mean  1010  and the sample standard deviation  1012 . 
     The sample mean is: 
     
       
         
           
             
               
                 
                   
                     
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                   3 
                 
               
             
           
         
       
     
     In Equation 2 and 3, N′ denotes the sample size of the sample comparison values  1004 , which is less than the actual total number of the comparison values  905  of  FIG. 9 . 
     One can make an assumption that the sample comparison values  1004  are distributed approximately normally because the normal distribution can be used to describe, at least approximately, any variable that tends to cluster around the mean. Under the assumption that the sample comparison values  1004  follow a normal distribution, an instance of the comparison values  905  corresponding to a percentile rank can be determined by a T-Table for the first normalized distribution  1002 . The percentile rank of a particular comparison value is defined as the probability that the particular comparison value in the first normal distribution  1002  is greater than or equal to all of the sample comparison values  1004  in the first normal distribution  1002 . 
     For example, if the first formatting sequence  710  of  FIG. 7  has the associated comparison value with the first cluster center  418  of  FIG. 4  of 0.9 and this value is greater than or equal to 88% of the sample comparison values  1004 , then the percentile rank of the formatting sequence associated with the cluster would be 88. The T-Table can convert the percentile rank to a multiplier  1022  of the sample standard deviation  1012 . With the multiplier  1022 , the sample mean  1010 , and the sample standard deviation  1012 , one can determine an instance of the comparison values  905  that correspond to the percentile rank. The instance of the comparison values  905  is equal to the sample mean  1010  plus the sample standard deviation  1012  multiply by the multiplier  1022 . 
     The percentile rank discussed above can be set to an intra-cluster similarity fraction, defined as equal to one over the total number of clusters, in order to calculate the threshold value  904  for the single pass clustering method  902 . The intra-similarity fraction is the fraction of comparison values  905  that are between formatting sequences in a particular cluster. Assuming all of the clusters  402  of  FIG. 4  have the same number of the formatting sequences  303 , then the intra-similarity fraction is one over the total number of the clusters  402 . 
     For example, the percentile rank can be set to a user-defined percentile  1014  representing reciprocal fraction  1016  of an expected number  1018  of the clusters  402 . The expected number  1018  of the clusters  402  is an approximation of how many clusters would be generated when clustering the formatting sequences  303 . The expected number  1018  of the clusters  402  can further be an approximation of how many types of the templates  302  of  FIG. 3  are used to generate the documents  201  of  FIG. 2 . The reciprocal fraction  1016  is simply defined as a numeric fraction of one over the expected number  1018 . The user-defined percentile  1014  is the percentile equivalent of the reciprocal fraction  1016 . 
     It has been discovered that the navigation system  100  provides an accurate clustering method for identifying templates to extract navigation-related information for display. The user-defined percentile  1014  is otherwise known as the intra-cluster similarity fraction, which has been discovered as a reliable and efficient threshold value for the single pass clustering method  902 . For example, a comparison value of a particular formatting sequence to the first cluster  404  that is greater than the user-defined percentile  1014  of the comparison values  905  more accurately suggests that the particular formatting sequence does not belong to the first cluster  404 . Using the threshold value  904  that corresponds to the user-defined percentile  1014  allows the single-pass clustering method  902  to more accurately cluster the formatting sequences  303 . The navigation system  100 , thereby, can use the single pass clustering method  902  together with the threshold value  904  reflecting the intra-cluster similarity to accurately group the formatting sequences  303 . 
     It has further been discovered that the navigation system  100  further provides a faster clustering method for identifying templates to extract navigation-related information for display and requires less computational power. To calculate the threshold value  904  corresponding to an intra-cluster similarity value can be computationally expensive when total number of the formatting sequences  303  is large. The navigation system  100  uses a fixed number of random samples to estimate the intra-cluster similarity and hence the threshold value, and thereby making the single pass clustering method  902  faster and requiring less computation power. 
     The multiplier  1022  of how many of the sample standard deviations  1012  above the sample mean  1010  corresponding to the intra-cluster similarity can be found by using a T-Table. The T-Table takes in variables such as degree of freedom df=N′−1, and the percentile rank, mapping these variables to the multiplier  1022  of the sample standard deviations  1012  above the sample mean  1010 . The computation of the threshold value  904  is described in Equation 4. The threshold value  904  is denoted by θ k  and the multiplier  1022  is denoted by t.
 
θ k   =  X     k   +tS   k .  Equation 4
 
     As a specific example, the navigation system  100  can be applied to the task of clustering template based web pages. More and more of documents on the World Wide Web are based on templates. On a technical level, this causes those documents to have a quite similar source code and DOM tree structure. 
     Grouping together documents, which are based on the same template, is an important step for information extraction. For instance, point of interest (POI) contents in the websites usually are semi-structured data, which are generated by machines, using a boiler-plate template for page layout and presentation (such as an XSLT file), and databases for actual content. So, the rate of success of POI extraction can be highly impacted by the template detection and clustering, and the different templates will lead to the different extraction rules. 
     In the example, the templates  302  of  FIG. 3  are represented by html tag sequences. The order of the tags in the html tag sequence is consistent with the topological order of the HTML DOM tree. The comparison values  905  of  FIG. 9  in the example can be local alignment scores of the tag sequence of the web page pairs. The local alignment scores will be discussed further a later part of the application. 
     As an example, the navigation system  100  operating the single pass clustering method  902  of  FIG. 9  on a dataset of 4260 webpages from www.tripadvisor.com contains four different templates. Six criteria of clustering quality, including purity, Normalized Mutual Information (NMI), Rand Index (RI), F score, precision and recall were used to evaluate the navigation system  100 . Purity, NMI, RI, and F measure are all evaluation measures. Bad clustering quality has measure values of purity, NMI, RI, and F measure that are close to 0, and perfect clustering usually has measure values of purity, NMI, RI, and F measure close to 1. Precision and recall are error rate measures, where inaccurate clustering has error rate measures close to 0, and accurate clustering has error rate measures close to 1. 
     Purity can be an external evaluation criterion for cluster quality, measuring whether the clusters  402  of  FIG. 4  contain one single class of the documents  201  of  FIG. 2 . Clusters containing documents from different classes have purity values close to 0, and clusters containing documents from all the same class have purity value close to 1. 
     NMI is a clustering quality measure similar to purity but penalizes having too many clusters. Clusters that perfectly recreate the classes of documents without extra clusters would obtain a NMI value of 1. 
     The Rand index (RI) measures the percentage of decisions that are correct. That is, it penalizes both false positive and false negative decisions during clustering. False positive (FP) decisions are where two dissimilar documents are assigned to the same cluster. False negative (FN) decisions are where two similar documents are assigned to different clusters. 
     RI is another useful method of evaluating the quality of the single pass clustering method  902  as operated by the navigation system  100 . The F measure supports differential weighting of false positives and false negatives, and generally penalizing false negatives more strongly than false positives. 
     The single pass clustering method  902  can be view as a series of decisions of between pairs of documents. Two documents to the same cluster are assigned if and only if they are similar. A true positive (TP) decision is where two similar documents are assigned to the same cluster; a true negative (TN) decision is where two dissimilar documents are assigned to different clusters. Two types of errors exist as described previously. A (FP) decision assigns two dissimilar documents to the same cluster. A (FN) decision assigns two similar documents to different clusters. So, the precision and recall are computed as in Equations 5 and 6: 
     
       
         
           
             
               
                 
                   
                     precision 
                     = 
                     
                       TP 
                       
                         TP 
                         + 
                         FP 
                       
                     
                   
                   , 
                 
               
               
                 
                   Equation 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   5 
                 
               
             
             
               
                 
                   
                     recall 
                     = 
                     
                       TP 
                       
                         TP 
                         + 
                         FN 
                       
                     
                   
                   , 
                 
               
               
                 
                   Equation 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   6 
                 
               
             
           
         
       
     
     The evaluation result for the example is shown as Table 1. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 The 6 external evaluation measures applied 
               
               
                 to the clustering, pre-defined M = 5 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                 F1 
                   
                   
               
               
                   
                 Purity 
                 NMI 
                 RI 
                 score 
                 Precision 
                 Recall 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Lower bound 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
               
               
                 Maximum 
                 1 
                 1 
                 1 
                 1 
                 1 
                 1 
               
               
                 Value for the 
                 0.90 
                 0.40 
                 0.64 
                 0.68 
                 0.87 
                 0.50 
               
               
                 clustering 
               
               
                   
               
            
           
         
       
     
     Table 1 shows that the specific example of the navigation system  100  operating single pass clustering method  902  has good purity and precision. Purity and precision reflects the accuracy of the process of creating POI extraction rules. Ideally, one template leads to one rule. If the cluster size is increased, though the recall might be improved, it will increase the risk of merging pages with different template into one cluster and bringing more noise into the POI extraction. If the cluster size is reduced, though there will be more clusters and lead to possible redundant extraction rules, the extraction performance will not be impacted much. 
     Referring now to  FIG. 11 , therein is shown a bar graph of the number of the clusters  402  of  FIG. 4  generated across examples of the navigation system  100  of  FIG. 1 . Stability is defined as how much results from the navigation system  100  changes for different ones of the user-defined percentile  1014  of  FIG. 10  or the user-defined expected number  1018  of  FIG. 10  of the clusters  402 . One measure of stability is to compare the differences between the numbers of clusters generated for different pre-set values of the expected number  1018  of the clusters  402 . Stability of the results across different runs can be an asset of a clustering method. 
       FIG. 11  illustrates the number of the clusters  402  generated across examples of the navigation system  100  for predefined M from 2 to 50.  FIG. 11  demonstrates that the navigation system  100  is generally stable, markedly so for larger M. 
     Referring now to  FIG. 12 , therein is shown a bar graph of multiple numbers of the threshold value  904  calculated for nine different clusters generated across five different examples of the navigation system  100 . The bar graph is an example of a measurement of clustering performance  1202  of the single pass clustering method  902  of  FIG. 9 . The bar graph illustrates that the navigation system  100  is generally stable as similar number of the clusters  402  of  FIG. 4  are generated across the five different examples with the threshold value  904  similar for each of the clusters  402 . 
     Referring now to  FIG. 13 , therein is shown an exemplary block diagram of the navigation system  100 . The navigation system  100  can include the first device  102  of  FIG. 1 , the communication path  104  of  FIG. 1 , and the second device  106  of  FIG. 1 . The first device  102  can send information in a first device transmission  1308  over the communication path  104  to the second device  106 . The second device  106  can send information in a second device transmission  1310  over the communication path  104  to the first device  102 . 
     For illustrative purposes, the navigation system  100  is shown with the first device  102  as a client device, although it is understood that the navigation system  100  can have the first device  102  as a different type of device. For example, the first device  102  can be a server. 
     Also for illustrative purposes, the navigation system  100  is shown with the second device  106  as a server, although it is understood that the navigation system  100  can have the second device  106  as a different type of device. For example, the second device  106  can be a client device. 
     For brevity of description in this embodiment of the present invention, the first device  102  will be described as a client device and the second device  106  will be described as a server device. The present invention is not limited to this selection for the type of devices. The selection is an example of the present invention. 
     The first device  102  can include a first control unit  1312 , a first storage unit  1314 , a first communication unit  1316 , a first user interface  1318 , and a location unit  1320 . The first control unit  1312  can include a first control interface  1322 . The first control unit  1312  can execute a first software  1326  to provide the intelligence of the navigation system  100 . The first control unit  1312  can be implemented in a number of different manners. For example, the first control unit  1312  can be a processor, an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), or a combination thereof. The first control interface  1322  can be used for communication between the first control unit  1312  and other functional units in the first device  102 . The first control interface  1322  can also be used for communication that is external to the first device  102 . 
     The first control interface  1322  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the first device  102 . 
     The first control interface  1322  can be implemented in different ways and can include different implementations depending on which functional units or external units are being interfaced with the first control interface  1322 . For example, the first control interface  1322  can be implemented with a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), optical circuitry, waveguides, wireless circuitry, wireline circuitry, or a combination thereof. 
     The location unit  1320  can generate location information, current heading, and current speed of the first device  102 , as examples. The location unit  1320  can be implemented in many ways. For example, the location unit  1320  can function as at least a part of a global positioning system (GPS), an inertial navigation system, a cellular-tower location system, a pressure location system, or any combination thereof. 
     The location unit  1320  can include a location interface  1332 . The location interface  1332  can be used for communication between the location unit  1320  and other functional units in the first device  102 . The location interface  1332  can also be used for communication that is external to the first device  102 . 
     The location interface  1332  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the first device  102 . 
     The location interface  1332  can include different implementations depending on which functional units or external units are being interfaced with the location unit  1320 . The location interface  1332  can be implemented with technologies and techniques similar to the implementation of the first control interface  1322 . 
     The first storage unit  1314  can store the first software  1326 . The first storage unit  1314  can also store the relevant information, such as advertisements, points of interest (POI), navigation routing entries, or any combination thereof. 
     The first storage unit  1314  can be a volatile memory, a nonvolatile memory, an internal memory, an external memory, or a combination thereof. For example, the first storage unit  1314  can be a nonvolatile storage such as non-volatile random access memory (NVRAM), Flash memory, disk storage, or a volatile storage such as static random access memory (SRAM). 
     The first storage unit  1314  can include a first storage interface  1324 . The first storage interface  1324  can be used for communication between the location unit  1320  and other functional units in the first device  102 . The first storage interface  1324  can also be used for communication that is external to the first device  102 . 
     The first storage interface  1324  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the first device  102 . 
     The first storage interface  1324  can include different implementations depending on which functional units or external units are being interfaced with the first storage unit  1314 . The first storage interface  1324  can be implemented with technologies and techniques similar to the implementation of the first control interface  1322 . 
     The first communication unit  1316  can enable external communication to and from the first device  102 . For example, the first communication unit  1316  can permit the first device  102  to communicate with the second device  106  of  FIG. 1 , an attachment, such as a peripheral device or a computer desktop, and the communication path  104 . 
     The first communication unit  1316  can also function as a communication hub allowing the first device  102  to function as part of the communication path  104  and not limited to be an end point or terminal unit to the communication path  104 . The first communication unit  1316  can include active and passive components, such as microelectronics or an antenna, for interaction with the communication path  104 . 
     The first communication unit  1316  can include a first communication interface  1328 . The first communication interface  1328  can be used for communication between the first communication unit  1316  and other functional units in the first device  102 . The first communication interface  1328  can receive information from the other functional units or can transmit information to the other functional units. 
     The first communication interface  1328  can include different implementations depending on which functional units are being interfaced with the first communication unit  1316 . The first communication interface  1328  can be implemented with technologies and techniques similar to the implementation of the first control interface  1322 . 
     The first user interface  1318  allows a user (not shown) to interface and interact with the first device  102 . The first user interface  1318  can include an input device and an output device. Examples of the input device of the first user interface  1318  can include a keypad, a touchpad, soft-keys, a keyboard, a microphone, or any combination thereof to provide data and communication inputs. 
     The first user interface  1318  can include a first display interface  1330 . The first display interface  1330  can include a display, a projector, a video screen, a speaker, or any combination thereof. 
     The first control unit  1312  can operate the first user interface  1318  to display information generated by the navigation system  100 . The first control unit  1312  can also execute the first software  1326  for the other functions of the navigation system  100 , including receiving location information from the location unit  1320 . The first control unit  1312  can further execute the first software  1326  for interaction with the communication path  104  via the first communication unit  1316 . 
     The second device  106  can be optimized for implementing the present invention in a multiple device embodiment with the first device  102 . The second device  106  can provide the additional or higher performance processing power compared to the first device  102 . The second device  106  can include a second control unit  1334 , a second communication unit  1336 , and a second user interface  1338 . 
     The second user interface  1338  allows a user (not shown) to interface and interact with the second device  106 . The second user interface  1338  can include an input device and an output device. Examples of the input device of the second user interface  1338  can include a keypad, a touchpad, soft-keys, a keyboard, a microphone, or any combination thereof to provide data and communication inputs. Examples of the output device of the second user interface  1338  can include a second display interface  1340 . The second display interface  1340  can include a display, a projector, a video screen, a speaker, or any combination thereof. 
     The second control unit  1334  can execute a second software  1342  to provide the intelligence of the second device  106  of the navigation system  100 . The second software  1342  can operate in conjunction with the first software  1326 . The second control unit  1334  can provide additional performance compared to the first control unit  1312 . 
     The second control unit  1334  can operate the second user interface  1338  to display information. The second control unit  1334  can also execute the second software  1342  for the other functions of the navigation system  100 , including operating the second communication unit  1336  to communicate with the first device  102  over the communication path  104 . 
     The second control unit  1334  can be implemented in a number of different manners. For example, the second control unit  1334  can be a processor, an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), or a combination thereof. 
     The second control unit  1334  can include a second controller interface  1344 . The second controller interface  1344  can be used for communication between the second control unit  1334  and other functional units in the second device  106 . The second controller interface  1344  can also be used for communication that is external to the second device  106 . 
     The second controller interface  1344  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the second device  106 . 
     The second controller interface  1344  can be implemented in different ways and can include different implementations depending on which functional units or external units are being interfaced with the second controller interface  1344 . For example, the second controller interface  1344  can be implemented with a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), optical circuitry, waveguides, wireless circuitry, wireline circuitry, or a combination thereof. 
     A second storage unit  1346  can store the second software  1342 . The second storage unit  1346  can also store the relevant information, such as advertisements, points of interest (POI), navigation routing entries, or any combination thereof. The second storage unit  1346  can be sized to provide the additional storage capacity to supplement the first storage unit  1314 . 
     For illustrative purposes, the second storage unit  1346  is shown as a single element, although it is understood that the second storage unit  1346  can be a distribution of storage elements. Also for illustrative purposes, the navigation system  100  is shown with the second storage unit  1346  as a single hierarchy storage system, although it is understood that the navigation system  100  can have the second storage unit  1346  in a different configuration. For example, the second storage unit  1346  can be formed with different storage technologies forming a memory hierarchal system including different levels of caching, main memory, rotating media, or off-line storage. 
     The second storage unit  1346  can be a volatile memory, a nonvolatile memory, an internal memory, an external memory, or a combination thereof. For example, the second storage unit  1346  can be a nonvolatile storage such as non-volatile random access memory (NVRAM), Flash memory, disk storage, or a volatile storage such as static random access memory (SRAM). 
     The second storage unit  1346  can include a second storage interface  1348 . The second storage interface  1348  can be used for communication between the location unit  1320  and other functional units in the second device  106 . The second storage interface  1348  can also be used for communication that is external to the second device  106 . 
     The second storage interface  1348  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the second device  106 . 
     The second storage interface  1348  can include different implementations depending on which functional units or external units are being interfaced with the second storage unit  1346 . The second storage interface  1348  can be implemented with technologies and techniques similar to the implementation of the second controller interface  1344 . 
     The second communication unit  1336  can enable external communication to and from the second device  106 . For example, the second communication unit  1336  can permit the second device  106  to communicate with the first device  102  over the communication path  104 . 
     The second communication unit  1336  can also function as a communication hub allowing the second device  106  to function as part of the communication path  104  and not limited to be an end point or terminal unit to the communication path  104 . The second communication unit  1336  can include active and passive components, such as microelectronics or an antenna, for interaction with the communication path  104 . 
     The second communication unit  1336  can include a second communication interface  1350 . The second communication interface  1350  can be used for communication between the second communication unit  1336  and other functional units in the second device  106 . The second communication interface  1350  can receive information from the other functional units or can transmit information to the other functional units. 
     The second communication interface  1350  can include different implementations depending on which functional units are being interfaced with the second communication unit  1336 . The second communication interface  1350  can be implemented with technologies and techniques similar to the implementation of the second controller interface  1344 . 
     The first communication unit  1316  can couple with the communication path  104  to send information to the second device  106  in the first device transmission  1308 . The second device  106  can receive information in the second communication unit  1336  from the first device transmission  1308  of the communication path  104 . 
     The second communication unit  1336  can couple with the communication path  104  to send information to the first device  102  in the second device transmission  1310 . The first device  102  can receive information in the first communication unit  1316  from the second device transmission  1310  of the communication path  104 . The navigation system  100  can be executed by the first control unit  1312 , the second control unit  1334 , or a combination thereof. 
     For illustrative purposes, the second device  106  is shown with the partition having the second user interface  1338 , the second storage unit  1346 , the second control unit  1334 , and the second communication unit  1336 , although it is understood that the second device  106  can have a different partition. For example, the second software  1342  can be partitioned differently such that some or all of its function can be in the second control unit  1334  and the second communication unit  1336 . Also, the second device  106  can include other functional units not shown in  FIG. 13  for clarity. 
     The functional units in the first device  102  can work individually and independently of the other functional units. The first device  102  can work individually and independently from the second device  106  and the communication path  104 . 
     The functional units in the second device  106  can work individually and independently of the other functional units. The second device  106  can work individually and independently from the first device  102  and the communication path  104 . 
     For illustrative purposes, the navigation system  100  is described by operation of the first device  102  and the second device  106 . It is understood that the first device  102  and the second device  106  can operate any of the modules and functions of the navigation system  100 . For example, the first device  102  is described to operate the location unit  1320 , although it is understood that the second device  106  can also operate the location unit  1320 . 
     Referring now to  FIG. 14 , therein is shown a control flow of the navigation system  100 . The navigation system  100  can include an extraction module  1402 . The extraction module  1402  locates documents on the World Wide Web or other sources that provide reviews such as the review  204  containing navigation-related information. For example, the extraction module  1402  can extract the documents  201  of  FIG. 2  having the point of interest  206  of  FIG. 2 . As another example, the extraction module  1402  can extract the navigation-related web documents  203  of  FIG. 2  having the point of interest  206 . 
     The navigation system  100  can include a feature generation module  1404 . The feature generation module  1404  generates each of the formatting sequences  303  of  FIG. 3  from each of the documents  201  by extracting the information contained in the standard protocols for formatting. For example, the feature generation module  1404  can generate the formatting sequence based on the XML or HTML source codes of the original document  202  of  FIG. 2  for display. The feature generation module  1404  can generate the formatting sequences  303  from the navigation-related web documents  203 . The feature generation module  1404  can also generate the formatting sequences  303  from the documents  201 . 
     The navigation system  100  can include a systems setting module  1406 . The systems setting module  1406  allows the user or the navigation system  100  to specify a percentile as an input to a threshold generator module  1410 . This percentile represents the reciprocal fraction of an expected number of clusters, and represents an estimation of the intra-cluster similarity, the fraction of comparison values  905  that are within a particular cluster. 
     The systems setting module  1406  can select the user-defined percentile  1014  of  FIG. 10  representing the reciprocal fraction  1016  of  FIG. 10  of the expected number  1018  of the clusters  402  of  FIG. 4 . The systems setting module  1406  can also modify the user-defined percentile  1014  based on the output of an evaluation module  1408 . 
     The navigation system  100  can include the evaluation module  1408 . The evaluation module  1408  evaluates the clustering performance by testing out the templates in order to adjust the user-defined percentile  1014  to maximize clustering performance and stability. The evaluation module  1408  can evaluate the clustering performance  1202  of  FIG. 12  to adjust the user-defined percentile  1014 . Clustering performance can include any one of purity, NMI, RI, F1 score, precision, and recall. 
     The navigation system  100  can include the threshold generator module  1410 . The threshold generator module  1410  calculates a threshold comparison value for a cluster that is used to determine whether a formatting sequence belong in the cluster. The threshold generator module  1410  can calculate the threshold value  904  of  FIG. 9  for the first cluster  404  of  FIG. 4  with the threshold value  904  to be equal to the user-defined percentile  1014  of the first normalized distribution  1002  of  FIG. 10  of the sample comparison values  1004  of  FIG. 10  between the first cluster  404  and the formatting sequence samples  1008  of  FIG. 10 . The first cluster  404  is from the clusters  402  of  FIG. 4 . The formatting sequence samples  1008  of  FIG. 10  are sampled from the formatting sequences  303  of  FIG. 3 . The threshold generator module  1410  can also calculate a further one of the threshold value  904  for the second cluster  412  of  FIG. 4 . 
     The navigation system  100  can include a comparison module  1412 . The comparison module  1412  computes numeric measure of how similar two documents are from each other by comparing the two documents&#39; formatting sequences. The comparison module  1412  can compute the associated comparison value  903  of  FIG. 9  between the first formatting sequence  710  of  FIG. 7  and the first cluster  404 . The comparison module  1412  includes a compare center module  1416  and an alignment score module  1418 . 
     The compare center module  1416  extracts a formatting sequence marked as the center for a cluster from the cluster. For example, the compare center module  1416  can extract the second cluster center  420  from the second cluster  412  or extract the first cluster center  418  of  FIG. 4  from the first cluster  404 . The compare center module  1416  can also compute comparison values between a cluster and a single formatting sequence by comparing the center of the cluster in the form of a formatting sequence, with the single formatting sequence. The compare center module  1416  also can compute the associated comparison value  903  by comparing the first formatting sequence  710  with the first cluster center  418 . 
     The alignment score module  1418  generates a local alignment score, an example of the associated comparison value, between a formatting sequence and another formatting sequence, such as between the first formatting sequence  710  and the first cluster center  418 . The local alignment score can be based on a calculation of what percentage of the formatting sequences in question match or overlap. The local alignment score can be based on other calculation involving how likely the formatting sequence can be aligned to match another formatting sequence. For example, the alignment score module  1418  can compute the associated comparison value  903  of  FIG. 9  based on aligning the first formatting sequence  710  with the first cluster center  418 . 
     The navigation system  100  can include a grouping module  1422 . The grouping module  1422  groups a formatting sequence with a cluster whenever the comparison value between the formatting sequence and the cluster exceeds the cluster&#39;s threshold. The grouping module  1422  can group the first formatting sequence  710  with the first cluster  404  when the associated comparison value  903  exceeds the threshold value  904  for the first cluster  404 . 
     The navigation system  100  can include a cluster insertion module  1424 . The cluster insertion module  1424  inserts a second cluster to the clusters whenever a formatting sequence fails to group with any cluster in the clusters. The cluster insertion module  1424  can insert the second cluster  412  to the clusters  402  containing the first formatting sequence  710  when the first formatting sequence  710  fails to group with any of existing clusters in the clusters  402 . 
     The cluster insertion module  1424  can include a center setting module  1426 . The center setting module  1426  sets the second cluster center  420  of  FIG. 4  as the formatting sequence first to be included in the second cluster  412 . The center setting module  1426  can set the first cluster center  418  as the first formatting sequence  710 , if the first formatting sequence  710  is the formatting sequence first to be included in the first cluster  404 . The center setting module  1426  can also set the second cluster center  420  as the first formatting sequence  710  if the first formatting sequence  710  is the formatting sequence first to be included in the second cluster  412 . 
     The navigation system  100  can include a route generation module  1428 . The route generation module  1428  generates a travel route based on the navigation-related information extracted from the documents  201 , where the extraction is based on the templates  302  generated for each of the clusters  402 . The route generation module  1428  can generate the travel route  806  of  FIG. 8  for the point of interest  206  of  FIG. 2  related to the first cluster  404  for displaying on the first device  102  of  FIG. 1  or the second device  106  of  FIG. 1 . The route generation module  1428  can also generate the travel route  806  for the point of interest  206  related to the second cluster  412  for displaying on the first device  102  or the second device  106 . 
     The route generation module  1428  can include a template ID module  1430 , a document parsing module  1432 , and a route extraction module  1434 . The template ID module  1430  creates the templates  302  from the formatting sequences  303  grouped together in the clusters  402 , each cluster having a matching template. The template ID module  1430  can identify the first template  304  matched with the first cluster  404 . The template ID module  1430  can also identify the second template  306  matched with the second cluster  412 . 
     The document parsing module  1432  parses the documents  201  within the clusters  402  with the templates  302  generated from the clusters  402 . The document parsing module  1432  can parse the point-of-interest page  602  of  FIG. 6  related to the point of interest  206  from the documents  201  by using the template  302 . 
     The route extraction module  1434  generates a travel route for the point of interest from the point-of-interest pages containing information relating to the point of interest. The route extraction module  1434  can generate the travel route  806  for the point of interest  206  from the point-of-interest page  602 . 
     The physical transformation from displaying the travel route  806  result in movement in the physical world, such as people operating the first device  102 , the vehicle, or a combination thereof, based on the operation of the navigation system  100 . The user of the navigation system  100  can select the point of interest  206  from the point-of-interest page  602  after considering the point-of-interest page  602  of  FIG. 6 . The current location  808  can start from where the user had selected the point of interest  206 , and the travel route  806  can start from the current location  808  of  FIG. 8  to the point of interest  206 . As the movement in the physical world occurs, the movement itself creates additional information that is converted back to the current location  808  for the continued operation of the navigation system  100  and to continue the movement in the physical world. 
     Referring now to  FIG. 15 , therein is shown a control flow of the threshold generator module  1410 . The threshold generator module  1410  includes a sampling module  1502 , a pairs generation module  1504 , a sample distribution module  1506 , a mean calculation module  1508 , a standard deviation calculation module  1510 , a multiplier calculation module  1512 , and a threshold setting module  1514 . 
     The sampling module  1502  samples the formatting sequences  303  of  FIG. 3  generating the formatting sequence samples  1008  of  FIG. 10 . The pairs generation module  1504  generates the matching pairs  1006  of  FIG. 10  by matching the first cluster  404  of  FIG. 4  with the formatting sequence samples  1008 . For example, if there are five sample formatting sequences, there will be five pairs generated. The sample distribution module  1506  computes the sample comparison values  1004  of  FIG. 10  from the matching pairs  1006  of  FIG. 10 . 
     The mean calculation module  1508  calculates the sample mean  1010  of  FIG. 10  of the sample comparison values  1004 . The standard deviation calculation module  1510  calculates the sample standard deviation  1012  of  FIG. 10  of the sample comparison values  1004 . The mean and the standard deviation are used to characterize the first normalized distribution  1002  of  FIG. 10 . The first normalized distribution  1002  follows a normal curve, and a specific comparison value can be determined for specific percentile of the first normalized distribution  1002 . 
     The multiplier calculation module  1512  sets the multiplier as how many standard deviations above the sample mean  1010  is the user-defined percentile  1014  of  FIG. 10  in the first normalized distribution  1002 . The threshold setting module  1514  sets the threshold value  904  to be the sample mean  1010  plus the sample standard deviation  1012  multiply by the multiplier  1022  of  FIG. 10 . 
     The first software  1326  of  FIG. 13  of the first device  102  of  FIG. 13  can include the navigation system  100 . For example, the first software  1326  can include the extraction module  1402 , the feature generation module  1404 , the systems setting module  1406 , the threshold generator module  1410 , the comparison module  1412 , the grouping module  1422 , and the route generation module  1428 . 
     The first control unit  1312  of  FIG. 13  can execute the first software  1326  for the extraction module  1402  to extract the navigation-related web documents  203 . The first control unit  1312  can execute the first software  1326  for the feature generation module  1404  to generate the formatting sequences  303 . The first control unit  1312  can execute the first software  1326  for the systems setting module  1406  to select the user-defined percentile  1014 . 
     The first control unit  1312  can execute the first software  1326  for the threshold generator module  1410  to calculate the threshold value  904 . The first control unit  1312  can execute the first software  1326  for the comparison module  1412  to compute the associated comparison value  903 . The first control unit  1312  can execute the first software  1326  for the grouping module  1422  to group the first formatting sequence  710  the first cluster  404 . 
     The first control unit  1312  can execute the first software  1326  for the route generation module  1428  to generate the travel route  806 . The first control unit  1312  can execute the first display interface  1330  of  FIG. 13  to display the travel route  806 . 
     The second software  1342  of  FIG. 13  of the second device  106  of  FIG. 13  can include the navigation system  100 . For example, the second software  1342  can include the extraction module  1402 , the feature generation module  1404 , the systems setting module  1406 , the threshold generator module  1410 , the comparison module  1412 , the grouping module  1422 , and the route generation module  1428 . 
     The second control unit  1334  of  FIG. 13  can execute the second software  1342  for the extraction module  1402  to extract the documents  201 . The second control unit  1334  can execute the second software  1342  for the feature generation module  1404  to generate the formatting sequences  303 . The second control unit  1334  can execute the second software  1342  for the systems setting module  1406  to select the user-defined percentile  1014 . 
     The second control unit  1334  can execute the second software  1342  for the threshold generator module  1410  to calculate the threshold value  904 . The second control unit  1334  can execute the second software  1342  for the comparison module  1412  to generate the revised associated comparison value. The second control unit  1334  can execute the second software  1342  for the grouping module  1422  to group the first formatting sequence  710  with the first cluster  404 . 
     The second control unit  1334  can execute the second software  1342  for the route generation module  1428  to generate the travel route  806 . The second control unit  1334  can execute the second display interface  1340  of  FIG. 13  to display the travel route  806 . 
     The navigation system  100  can be partitioned between the first software  1326  and the second software  1342 . For example, the second software  1342  can include the feature generation module  1404 , the systems setting module  1406 , the threshold generator module  1410 , the comparison module  1412 , the grouping module  1422 , and the route generation module  1428 . The second control unit  1334  can execute modules partitioned on the second software  1342  as previously described. 
     The first software  1326  can include the extraction module  1402 . Based on the size of the first storage unit  1314 , the first software  1326  can include additional modules of the navigation system  100 . The first control unit  1312  can execute the modules partitioned on the first software  1326  as previously described. 
     The first user interface  1318  of  FIG. 13  can receive the entry  804  by the user, the navigation system  100 , or a combination thereof for the extraction module  1402 . The first control unit  1312  can operate the first communication unit  1316  to send the entry  804  to the second device  106 . The first control unit  1312  can operate the first software  1326  to operate the location unit  1320 . 
     The second communication unit  1336  of  FIG. 13  can send the travel route  806  to the first device  102  through the communication path  104  of  FIG. 13 . The travel route  806  can be displayed on the first display interface  1330  and the second device  106 . 
     It has been discovered that the present invention provides the navigation system  100  for a safer operation of the vehicle, the navigation system  100 , and other user interface system within the vehicle. By generating the templates  302  matched to the clusters  402 , the navigation system  100  can parse the documents  201  using the templates  302  accurately, and display the point-of-interest page  602  and the travel route  806  accurately. The displaying of the travel guide  702  including the point-of-interest page  602  and the travel route  806  reduces the exposure of harm by permitting the user to make decision for traveling to the point of interest  206  based on accurate information. As a result, displaying the travel guide  702  can aid the user of safer operation of the vehicle. 
     The navigation system  100  describes the module functions or order as an example. The modules can be partitioned differently. For example, the mean calculation module  1508  and the standard deviation calculation module  1510  can be combined. Each of the modules can operate individually and independently of the other modules. 
     Furthermore, data generated in one module can be used by another module without being directly coupled to each other. For example, the route generation module  1428  can receive the entry  804  for the point of interest  206  from the extraction module  1402 . 
     Referring now to  FIG. 16 , therein is shown a flow chart of a method  1600  of operation of the navigation system  100  in a further embodiment of the present invention. The method  900  includes: extracting navigation-related web documents having a point of interest in a block  1602 ; generating formatting sequences of the navigation-related web documents in a block  1604 ; selecting a user-defined percentile representing reciprocal fraction of an expected number of clusters in a block  1606 ; calculating a threshold value for a first cluster with the threshold value to be equal to the user-defined percentile of a first normalized distribution of sample comparison values between the first cluster and formatting sequence samples from the formatting sequences, the first cluster is from the clusters in a block  1608 ; computing an associated comparison value between a first formatting sequence from the formatting sequences and the first cluster in a block  1610 ; grouping the first formatting sequence with the first cluster when the associated comparison value exceeds the threshold value for the first cluster in a block  1612 ; and generating a travel route for the point of interest related to the first cluster for displaying on a device in a block  1614 . 
     The resulting method, process, apparatus, device, product, and/or system is straightforward, cost-effective, uncomplicated, highly versatile, accurate, sensitive, and effective, and can be implemented by adapting known components for ready, efficient, and economical manufacturing, application, and utilization. Another important aspect of the present invention is that it valuably supports and services the historical trend of reducing costs, simplifying systems, and increasing performance. These and other valuable aspects of the present invention consequently further the state of the technology to at least the next level. 
     While the invention has been described in conjunction with a specific best mode, it is to be understood that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the aforegoing description. Accordingly, it is intended to embrace all such alternatives, modifications, and variations that fall within the scope of the included claims. All matters hithertofore set forth herein or shown in the accompanying drawings are to be interpreted in an illustrative and non-limiting sense.