PATENT ABSTRACT
A computer-implemented system and method for generating clusters for placement into a display is provided. A set of clusters is generated from a document set. A single cluster of related documents from the document set is obtained and at least one new cluster is added. One such document in the set is compared to the cluster. A difference in distance between the document and a common origin and the cluster and the common origin is determined. The document is designated as the new cluster when the difference fails to satisfy a predetermined threshold. One or more cluster spines each having two or more clusters placed along a vector are placed into a display. The clusters along each spine are identified as similar and the clusters of one such spine are also similar to further clusters located along a further spine having a small cosine rotation from that cluster spine.

PATENT DESCRIPTION
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This non-provisional patent application is a continuation of U.S. Pat. No. 9,208,221, issued Dec. 8, 2015, which is a continuation of U.S. Pat. No. 8,650,190, issued Feb. 11, 2014, which is a continuation of U.S. Pat. No. 8,402,026, issued Mar. 19, 2013, which is a continuation of U.S. Pat. No. 6,778,995, issued Aug. 17, 2004, the priority dates of which are claimed and the disclosures of which are incorporated by reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates in general to text mining and, in particular, to a computer-implemented system and method for generating clusters for placement into a display. 
     BACKGROUND OF THE INVENTION 
     Document warehousing extends data warehousing to content mining and retrieval. Document warehousing attempts to extract semantic information from collections of unstructured documents to provide conceptual information with a high degree of precision and recall. Documents in a document warehouse share several properties. First, the documents lack a common structure or shared type. Second, semantically-related documents are integrated through text mining. Third, essential document features are extracted and explicitly stored as part of the document warehouse. Finally, documents are often retrieved from multiple and disparate sources, such as over the Internet or as electronic messages. 
     Document warehouses are built in stages to deal with a wide range of information sources. First, document sources are identified and documents are retrieved into a repository. For example, the document sources could be electronic messaging folders or Web content retrieved over the Internet. Once retrieved, the documents are pre-processed to format and regularize the information into a consistent manner. Next, during text analysis, text mining is performed to extract semantic content, including identifying dominant themes, extracting key features and summarizing the content. Finally, metadata is compiled from the semantic context to explicate essential attributes. Preferably, the metadata is provided in a format amenable to normalized queries, such as database management tools. Document warehousing is described in D. Sullivan, “Document Warehousing and Text Mining, Techniques for Improving Business Operations, Marketing, and Sales,” Chs. 1-3, Wiley Computer Publishing (2001), the disclosure of which is incorporated by reference. 
     Text mining is at the core of the data warehousing process. Text mining involves the compiling, organizing and analyzing of document collections to support the delivery of targeted types of information and to discover relationships between relevant facts. However, identifying relevant content can be difficult. First, extracting relevant content requires a high degree of precision and recall. Precision is the measure of how well the documents returned in response to a query actually address the query criteria. Recall is the measure of what should have been returned by the query. Typically, the broader and less structured the documents, the lower the degree of precision and recall. Second, analyzing an unstructured document collection without the benefit of a priori knowledge in the form of keywords and indices can present a potentially intractable problem space. Finally, synonymy and polysemy can cloud and confuse extracted content. Synonymy refers to multiple words having the same meaning and polysemy refers to a single word with multiple meanings. Fine-grained text mining must reconcile synonymy and polysemy to yield meaningful results. 
     In particular, the transition from syntactic to semantic content analysis requires a shift in focus from the grammatical level to the meta level. At a syntactic level, documents are viewed structurally as sentences comprising individual terms and phrases. In contrast, at a semantic level, documents are viewed in terms of meaning. Terms and phrases are grouped into clusters representing individual concepts and themes. 
     Data clustering allows the concepts and themes to be developed more fully based on the extracted syntactic information. A balanced set of clusters reflects terms and phrases from every document in a document set. Each document may be included in one or more clusters. Conversely, concepts and themes are preferably distributed over a meaningful range of clusters. 
     Creating an initial set of clusters from a document set is crucial to properly visualizing the semantic content. Generally, a priori knowledge of semantic content is unavailable when forming clusters from unstructured documents. The difficulty of creating an initial clusters set is compounded when evaluating different types of documents, such as electronic mail (email) and word processing documents, particularly when included in the same document set. 
     In the prior art, several data clustering techniques are known. Exhaustive matching techniques fit each document into one of a pre-defined and fixed number of clusters using a closest-fit approach. However, this approach forces an arbitrary number of clusters onto a document set and can skew the meaning of the semantic content mined from the document set. 
     A related prior art clustering technique performs gap analysis in lieu of exhaustive matching. Gaps in the fit of points of data between successive passes are merged if necessary to form groups of documents into clusters. However, gap analysis is computational inefficient, as multiple passes through a data set are necessary to effectively find a settled set of clusters. 
     Therefore, there is a need for an approach to forming clusters of concepts and themes into groupings of classes with shared semantic meanings. Such an approach would preferably categorize concepts mined from a document set into clusters defined within a pre-specified range of variance. Moreover, such an approach would not require a priori knowledge of the data content. 
     SUMMARY OF THE INVENTION 
     The present invention provides a system and method for generating logical clusters of documents in a multi-dimensional concept space for modeling semantic meaning. Each document in a set of unstructured documents is first analyzed for syntactic content by extracting literal terms and phrases. The semantic content is then determined by modeling the extracted terms and phrases in multiple dimensions. Histograms of the frequency of occurrences of the terms and phrases in each document and over the entire document set are generated. Related documents are identified by finding highly correlated term and phrase pairings. These pairings are then used to calculate Euclidean distances between individual documents. Those documents corresponding to concepts separated by a Euclidean distance falling within a predetermined variance are grouped into clusters by k-means clustering. The remaining documents are grouped into new clusters. The clusters can be used to visualize the semantic content. 
     An embodiment provides a computer-implemented system and method for generating clusters for placement into a display. A set of clusters is generated from a document set. A single cluster of related documents from the document set is obtained and at least one new cluster is added. One such document in the set is compared to the cluster. A difference in distance is determined between the document and a common origin and the cluster and the common origin. The document is designated as the new cluster when the difference fails to satisfy a predetermined threshold. One or more cluster spines each having two or more clusters placed along a vector are placed into a display. The clusters along each spine are identified as similar and the clusters of one such spine are also similar to further clusters located along a further spine having a small cosine rotation from that cluster spine. 
     Still other embodiments of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein is described embodiments of the invention by way of illustrating the best mode contemplated for carrying out the invention. As will be realized, the invention is capable of other and different embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and the scope of the present invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram showing a system for efficiently generating cluster groupings in a multi-dimensional concept space, in accordance with the present invention. 
         FIG. 2  is a block diagram showing the software modules implementing the document analyzer of  FIG. 1 . 
         FIG. 3  is a process flow diagram showing the stages of text analysis performed by the document analyzer of  FIG. 1 . 
         FIG. 4  is a flow diagram showing a method for efficiently generating cluster groupings in a multi-dimensional concept space, in accordance with the present invention. 
         FIG. 5  is a flow diagram showing the routine for performing text analysis for use in the method of  FIG. 4 . 
         FIG. 6  is a flow diagram showing the routine for creating a histogram for use in the routine of  FIG. 5 . 
         FIG. 7  is a data structure diagram showing a database record for a concept stored in the database  30  of  FIG. 1 . 
         FIG. 8  is a data structure diagram showing, by way of example, a database table containing a lexicon of extracted concepts stored in the database  30  of  FIG. 1 . 
         FIG. 9  is a graph showing, by way of example, a histogram of the frequencies of concept occurrences generated by the routine of  FIG. 6 . 
         FIG. 10  is a table showing, by way of example, concept occurrence frequencies generated by the routine of  FIG. 6 . 
         FIG. 11  is a graph showing, by way of example, a corpus graph of the frequency of concept occurrences generated by the routine of  FIG. 5 . 
         FIG. 12  is a flow diagram showing the routine for creating clusters for use in the routine of  FIG. 5 . 
         FIG. 13  is a table showing, by way of example, the concept clusters created by the routine for  FIG. 12 . 
         FIG. 14  is a data representation diagram showing, by way of example, a view of overlapping cluster generated by the system of  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION 
     Glossary 
     
         
         
           
             Keyword: A literal search term which is either present or absent from a document. Keywords are not used in the evaluation of documents as described herein. 
             Term: A root stem of a single word appearing in the body of at least one document. 
             Phrase: Two or more words co-occurring in the body of a document. A phrase can include stop words. 
             Concept: A collection of terms or phrases with common semantic meanings 
             Theme: Two or more concepts with a common semantic meaning 
             Cluster: All documents for a given concept or theme.
 
The foregoing terms are used throughout this document and, unless indicated otherwise, are assigned the meanings presented above.
 
           
         
       
    
       FIG. 1  is a block diagram showing a system  11  for efficiently generating cluster groupings in a multi-dimensional concept space, in accordance with the present invention. By way of illustration, the system  11  operates in a distributed computing environment  10 , which includes a plurality of heterogeneous systems and document sources. The system  11  implements a document analyzer  12 , as further described below beginning with reference to  FIG. 2 , for evaluating latent concepts in unstructured documents. The system  11  is coupled to a storage device  13 , which stores a document warehouse  14  for maintaining a repository of documents and a database  30  for maintaining document information. 
     The document analyzer  12  analyzes documents retrieved from a plurality of local sources. The local sources include documents  17  maintained in a storage device  16  coupled to a local server  15  and documents  20  maintained in a storage device  19  coupled to a local client  18 . The local server  15  and local client  18  are interconnected to the system  11  over an intranetwork  21 . In addition, the document analyzer  12  can identify and retrieve documents from remote sources over an internetwork  22 , including the Internet, through a gateway  23  interfaced to the intranetwork  21 . The remote sources include documents  26  maintained in a storage device  25  coupled to a remote server  24  and documents  29  maintained in a storage device  28  coupled to a remote client  27 . 
     The individual documents  17 ,  20 ,  26 ,  29  include all forms and types of unstructured data, including electronic message stores, such as electronic mail (email) folders, word processing documents or Hypertext documents, and could also include graphical or multimedia data. Notwithstanding, the documents could be in the form of structured data, such as stored in a spreadsheet or database. Content mined from these types of documents does not require preprocessing, as described below. 
     In the described embodiment, the individual documents  17 ,  20 ,  26 ,  29  include electronic message folders, such as maintained by the Outlook and Outlook Express products, licensed by Microsoft Corporation, Redmond, Wash. The database is an SQL-based relational database, such as the Oracle database management system, release 8, licensed by Oracle Corporation, Redwood Shores, Calif. 
     The individual computer systems, including system  11 , server  15 , client  18 , remote server  24  and remote client  27 , are general purpose, programmed digital computing devices consisting of a central processing unit (CPU), random access memory (RAM), non-volatile secondary storage, such as a hard drive or CD ROM drive, network interfaces, and peripheral devices, including user interfacing means, such as a keyboard and display. Program code, including software programs, and data are loaded into the RAM for execution and processing by the CPU and results are generated for display, output, transmittal, or storage. 
       FIG. 2  is a block diagram showing the software modules  40  implementing the document analyzer  12  of  FIG. 1 . The document analyzer  12  includes three modules: storage and retrieval manager  41 , text analyzer  42 , and display and visualization  44 . The storage and retrieval manager  41  identifies and retrieves documents  45  into the document warehouse  14  (shown in  FIG. 1 ). The documents  45  are retrieved from various sources, including both local and remote clients and server stores. The text analyzer  42  performs the bulk of the text mining processing. The cluster  43  generates clusters  49  of highly correlated documents, as further described below with reference to  FIG. 12 . The display and visualization  44  complements the operations performed by the text analyzer  42  by presenting visual representations of the information extracted from the documents  45 . The display and visualization  44  can also generate a graphical representation which preserves independent variable relationships, such as described in common-assigned U.S. Pat. No. 6,888,548, issued May 3, 2005, the disclosure of which is incorporated by reference. 
     During text analysis, the text analyzer  42  identifies terms and phrases and extracts concepts in the form of noun phrases that are stored in a lexicon  18  maintained in the database  30 . After normalizing the extracted concepts, the text analyzer  42  generates a frequency table  47  of concept occurrences, as further described below with reference to  FIG. 6 , and a matrix  48  of summations of the products of pair-wise terms, as further described below with reference to  FIG. 10 . The cluster  43  generates logical clusters  49  of documents in a multi-dimensional concept space for modeling semantic meaning. Similarly, the display and visualization  44  generates a histogram  50  of concept occurrences per document, as further described below with reference to  FIG. 6 , and a corpus graph  51  of concept occurrences over all documents, as further described below with reference to  FIG. 8 . 
     Each module is a computer program, procedure or module written as source code in a conventional programming language, such as the C++ programming language, and is presented for execution by the CPU as object or byte code, as is known in the art. The various implementations of the source code and object and byte codes can be held on a computer-readable storage medium or embodied on a transmission medium in a carrier wave. The document analyzer  12  operates in accordance with a sequence of process steps, as further described below with reference to  FIG. 5 . 
       FIG. 3  is a process flow diagram showing the stages  60  of text analysis performed by the document analyzer  12  of  FIG. 1 . The individual documents  45  are preprocessed and noun phrases are extracted as concepts (transition  61 ) into a lexicon  46 . The noun phrases are normalized and queried (transition  62 ) to generate a frequency table  47 . The frequency table  47  identifies individual concepts and their respective frequency of occurrence within each document  45 . The frequencies of concept occurrences are visualized (transition  63 ) into a frequency of concepts histogram  50 . The histogram  50  graphically displays the frequencies of occurrence of each concept on a per-document basis. Next, the frequencies of concept occurrences for all the documents  45  are assimilated (transition  64 ) into a corpus graph  51  that displays the overall counts of documents containing each of the extracted concepts. Finally, the most highly correlated terms and phrases from the extracted concepts are categorized (transition  65 ) into clusters  49 . 
       FIG. 4  is a flow diagram showing a method  70  for efficiently generating cluster groupings in a multi-dimensional concept space  44  (shown in  FIG. 2 ), in accordance with the present invention. As a preliminary step, the set of documents  45  to be analyzed is identified (block  71 ) and retrieved into the document warehouse  14  (shown in  FIG. 1 ) (block  72 ). The documents  45  are unstructured data and lack a common format or shared type. The documents  45  include electronic messages stored in messaging folders, word processing documents, hypertext documents, and the like. 
     Once identified and retrieved, the set of documents  45  is analyzed (block  73 ), as further described below with reference to  FIG. 5 . During text analysis, a matrix  48  (shown in  FIG. 2 ) of term-document association data is constructed to summarize the semantic content inherent in the structure of the documents  45 . The semantic content is represented by groups of clusters of highly correlated documents generated through k-means clustering. As well, the frequency of individual terms or phrases extracted from the documents  45  are displayed and the results, including the clusters  43 , are optionally visualized (block  74 ), as further described below with reference to  FIG. 14 . The routine then terminates. 
       FIG. 5  is a flow diagram showing the routine  80  for performing text analysis for use in the method  70  of  FIG. 4 . The purpose of this routine is to extract and index terms or phrases for the set of documents  45  (shown in  FIG. 2 ). Preliminarily, each document in the documents set  44  is preprocessed (block  81 ) to remove stop words. These include commonly occurring words, such as indefinite articles (“a” and “an”), definite articles (“the”), pronouns (“I”, “he” and “she”), connectors (“and” and “or”), and similar non-substantive words. 
     Following preprocessing, a histogram  50  of the frequency of terms (shown in  FIG. 2 ) is logically created for each document  45  (block  82 ), as further described below with reference to  FIG. 6 . Each histogram  50 , as further described below with reference to  FIG. 9 , maps the relative frequency of occurrence of each extracted term on a per-document basis. 
     Next, a document reference frequency (corpus) graph  51 , as further described below with reference to  FIG. 10 , is created for all documents  45  (block  83 ). The corpus graph  51  graphically maps the semantically-related concepts for the entire documents set  44  based on terms and phrases. A subset of the corpus is selected by removing those terms and phrases falling outside either edge of predefined thresholds (block  84 ). For shorter documents, such as email, having less semantically-rich content, the thresholds are set from about 1% to about 15%, inclusive. Larger documents may require tighter threshold values. 
     The selected set of terms and phrases falling within the thresholds are used to generate themes (and concepts) (block  85 ) based on correlations between normalized terms and phrases in the documents set. In the described embodiment, themes are primarily used, rather than individual concepts, as a single co-occurrence of terms or phrases carries less semantic meaning than multiple co-occurrences. As used herein, any reference to a “theme” or “concept” will be understood to include the other term, except as specifically indicated otherwise. 
     Next, clusters of concepts and themes are created (block  86 ) from groups of highly-correlated terms and phrases, as further described below with reference to  FIG. 12 . The routine then returns. 
       FIG. 6  is a flow diagram showing the routine  90  for creating a histogram  50  (shown in  FIG. 2 ) for use in the routine of  FIG. 5 . The purpose of this routine is to extract noun phrases representing individual concepts and to create a normalized representation of the occurrences of the concepts on a per-document basis. The histogram represents the logical union of the terms and phrases extracted from each document. In the described embodiment, the histogram  48  need not be expressly visualized, but is generated internally as part of the text analysis process. 
     Initially, noun phrases are extracted (block  91 ) from each document  45 . In the described embodiment, concepts are defined on the basis of the extracted noun phrases, although individual nouns or tri-grams (word triples) could be used in lieu of noun phrases. In the described embodiment, the noun phrases are extracted using the LinguistX product licensed by Inxight Software, Inc., Santa Clara, Calif. 
     Once extracted, the individual terms or phrases are loaded into records stored in the database  30  (shown in  FIG. 1 ) (block  92 ). The terms stored in the database  30  are normalized (block  93 ) such that each concept appears as a record only once. In the described embodiment, the records are normalized into third normal form, although other normalization schemas could be used. 
       FIG. 7  is a data structure diagram showing a database record  100  for a concept stored in the database  30  of  FIG. 1 . Each database record  100  includes fields for storing an identifier  101 , string  102  and frequency  103 . The identifier  101  is a monotonically increasing integer value that uniquely identifies each term or phrase stored as the string  102  in each record  100 . The frequency of occurrence of each term or phrase is tallied in the frequency  103 . 
       FIG. 8  is a data structure diagram showing, by way of example, a database table  110  containing a lexicon  111  of extracted concepts stored in the database  30  of  FIG. 1 . The lexicon  111  maps out the individual occurrences of identified terms  113  extracted for any given document  112 . By way of example, the document  112  includes three terms numbered 1, 3 and 5. Concept 1 occurs once in document  112 , concept 3 occurs twice, and concept 5 occurs once. The lexicon tallies and represents the occurrences of frequency of the concepts 1, 3 and 5 across all documents  45 . 
     Referring back to  FIG. 6 , a frequency table is created from the lexicon  111  for each given document  45  (block  94 ). The frequency table is sorted in order of decreasing frequencies of occurrence for each concept  113  found in a given document  45 . In the described embodiment, all terms and phrases occurring just once in a given document are removed as not relevant to semantic content. The frequency table is then used to generate a histogram  50  (shown in  FIG. 2 ) (block  95 ) which visualizes the frequencies of occurrence of extracted concepts in each document. The routine then returns. 
       FIG. 9  is a graph showing, by way of example, a histogram  50  of the frequencies of concept occurrences generated by the routine of  FIG. 6 . The x-axis defines the individual concepts  121  for each document and the y-axis defines the frequencies of occurrence of each concept  122 . The concepts are mapped in order of decreasing frequency  123  to generate a curve  124  representing the semantic content of the document  45 . Accordingly, terms or phrases appearing on the increasing end of the curve  124  have a high frequency of occurrence while concepts appearing on the descending end of the curve  124  have a low frequency of occurrence. 
       FIG. 10  is a table  130  showing, by way of example, concept occurrence frequencies generated by the routine of  FIG. 6 . Each concept  131  is mapped against the total frequency occurrence  132  for the entire set of documents  45 . Thus, for each of the concepts  133 , a cumulative frequency  134  is tallied. The corpus table  130  is used to generate the document concept frequency reference (corpus) graph  51 . 
       FIG. 11  is a graph  140  showing, by way of example, a corpus graph of the frequency of concept occurrences generated by the routine of  FIG. 5 . The graph  140  visualizes the extracted concepts as tallied in the corpus table  130  (shown in  FIG. 10 ). The x-axis defines the individual concepts  141  for all documents and the y-axis defines the number of documents  45  referencing each concept  142 . The individual concepts are mapped in order of descending frequency of occurrence  143  to generate a curve  144  representing the latent semantics of the set of documents  45 . 
     A median value  145  is selected and edge conditions  146   a - b  are established to discriminate between concepts which occur too frequently versus concepts which occur too infrequently. Those documents falling within the edge conditions  146   a - b  form a subset of documents containing latent concepts. In the described embodiment, the median value  145  is document-type dependent. For efficiency, the upper edge condition  146   b  is set to 70% and the  64  concepts immediately preceding the upper edge condition  146   b  are selected, although other forms of threshold discrimination could also be used. 
       FIG. 12  is a flow diagram  150  showing the routine for creating clusters for use in the routine of  FIG. 5 . The purpose of this routine is to build a concept space over a document collection consisting of clusters  49  (shown in  FIG. 2 ) of individual documents having semantically similar content. Initially, a single cluster is created and additional clusters are added using a k-mean clustering technique, as required by the document set. Those clusters falling outside a pre-determined variance are grouped into new clusters, such that every document in the document set appears in at least one cluster and the concepts and themes contained therein are distributed over a meaningful range of clusters. The clusters are then visualized as a data representation, as further described below with reference to  FIG. 14 . 
     Each cluster consists of a set of documents that share related terms and phrases as mapped in a multi-dimensional concept space. Those documents having identical terms and phrases mapped to a single cluster located along a vector at a distance (magnitude) d measured at an angle θ from a common origin relative to the multi-dimensional concept space. Accordingly, a Euclidean distance between the individual concepts can be determined and clusters created. 
     Initially, a variance specifying an upper bound on Euclidean distances in the multi-dimensional concept space is determined (block  151 ). In the described embodiment, a variance of five percent is specified, although other variance values, either greater or lesser than five percent, could be used as appropriate to the data profile. As well, an internal counter num_clusters is set to the initial value of 1 (block  152 ). 
     The documents and clusters are iteratively processed in a pair of nested processing loops (blocks  153 - 164  and  156 - 161 ). During each iteration of the outer processing loop (blocks  153 - 164 ), each document i is processed (block  153 ) for every document in the document set. Each document i is first selected (block  154 ) and the angle θ relative to a common origin is computed (block  155 ). 
     During each iterative loop of the inner processing loop (block  156 - 161 ), the selected document i is compared to the existing set of clusters. Thus, a cluster j is selected (block  157 ) and the angle σ relative to the common origin is computed (block  158 ). Note the angle σ must be recomputed regularly for each cluster j as documents are added or removed. The difference between the angle θ for the document i and the angle σ for the cluster j is compared to the predetermined variance (block  159 ). If the difference is less than the predetermined variance (block  159 ), the document i is put into the cluster j (block  160 ) and the iterative processing loop (block  156 - 161 ) is terminated. If the difference is greater than or equal to the variance (block  159 ), the next cluster j is processed (block  161 ) and processing continues for each of the current clusters (blocks  156 - 161 ). 
     If the difference between the angle θ for the document i and the angle σ for each of the clusters exceeds the variance, a new cluster is created (block  162 ) and the counter num_clusters is incremented (block  163 ). Processing continues with the next document i (block  164 ) until all documents have been processed (blocks  153 - 164 ). The categorization of clusters is repeated (block  165 ) if necessary. In the described embodiment, the cluster categorization (blocks  153 - 164 ) is repeated at least once until the set of clusters settles. Finally, the clusters can be finalized (block  165 ) as an optional step. Finalization includes merging two or more clusters into a single cluster, splitting a single cluster into two or more clusters, removing minimal or outlier clusters, and similar operations, as would be recognized by one skilled in the art. The routine then returns. 
       FIG. 13  is a table  180  showing, by way of example, the concept clusters created by the routine  150  of  FIG. 12 . Each of the concepts  181  should appear in at least one of the clusters  182 , thereby insuring that each document appears in some cluster. The Euclidean distances  183   a - d  between the documents for a given concept are determined. Those Euclidean distances  183   a - d  falling within a predetermined variance are assigned to each individual cluster  184 - 186 . The table  180  can be used to visualize the clusters in a multi-dimensional concept space. 
       FIG. 14  is a data representation diagram  14  showing, by way of example, a view  191  of overlapping clusters  193 - 196  generated by the system of  FIG. 1 . Each cluster  193 - 196  has a center c  197 - 200  and radius r  201 - 204 , respectively, and is oriented around a common origin  192 . The center c of each cluster  193 - 196  is located at a fixed distance d  205 - 208  from the common origin  192 . Cluster  194  overlays cluster  193  and clusters  193 ,  195  and  196  overlap. 
     Each cluster  193 - 196  represents multi-dimensional data modeled in a three-dimensional display space. The data could be visualized data for a virtual semantic concept space, including semantic content extracted from a collection of documents represented by weighted clusters of concepts, such as described in commonly-assigned U.S. Pat. No. 6,978,274, issued Dec. 20, 2005, the disclosure of which is incorporated by reference. 
     For each cluster  193 , the radii r  201 - 204  and distances d  197 - 200  are independent variables relative to the other clusters  194 - 196  and the radius r  201  is an independent variable relative to the common origin  192 . In this example, each cluster  193 - 196  represents a grouping of points corresponding to documents sharing a common set of related terms and phrases. The radii  201 - 204  of each cluster  193 - 196  reflect the relative number of documents contained in each cluster. Those clusters  193 - 197  located along the same vector are similar in theme as are those clusters located on vectors having a small cosign rotation from each other. Thus, the angle θ relative to a common axis&#39; distance from a common origin  192  is an independent variable within a correlation between the distance d and angle θ relative similarity of theme. Although shown with respect to a circular shape, each cluster  193 - 196  could be non-circular. At a minimum, however, each cluster  193 - 196  must have a center of mass and be oriented around the common origin  192  and must define a convex volume. Accordingly, other shapes defining each cluster  193 - 196  are feasible. 
     While the invention has been particularly shown and described as referenced to the embodiments thereof, those skilled in the art will understand that the foregoing and other changes in form and detail may be made therein without departing from the spirit and scope of the invention.