Patent Publication Number: US-2007112867-A1

Title: Methods and apparatus for rank-based response set clustering

Description:
BACKGROUND  
      1. Field of the Invention  
      The present disclosure relates to computerized analysis of documents, and in particular, to identifying clusters of similar documents from among a set of documents.  
      2. Background Information  
      Rapid growth in the quantity of unstructured electronic text has increased the importance of efficient and accurate document clustering. By clustering similar documents, users can explore topics in a collection without reading large numbers of documents. Organizing search results into meaningful flat or hierarchical structures can help users navigate, visualize, and summarize what would otherwise be an impenetrable mountain of data.  
      Hierarchical (agglomerative and divisive) clustering methods are known. Hierarchical agglomerative clustering (HAC) starts with the documents as individual clusters and successively merges the most similar pair of clusters. Hierarchical divisive clustering (HDC) starts with one cluster of all documents and successively splits the least uniform clusters. A problem for all HAC and HDC methods is their high computational complexity (O(n 2 ) or even O(n 3 )), which makes them unscaleable in practice.  
      Partitional clustering methods based on iterative relocation are also known. To construct K clusters, a partitional method creates all K groups at once and then iteratively improves the partitioning by moving documents from one group to another in order to optimize a selected criterion function. Major disadvantages of such methods include the need to specify the number of clusters in advance, assumption of uniform cluster size, and sensitivity to noise.  
      Density-based partitioning methods for clustering are also known. Such methods define clusters as densely populated areas in a space of attributes, surrounded by noise, i.e., data points not contained in any cluster. These methods are targeted at primarily low-dimensional data.  
      Despite these and other clustering approaches known from the literature, efficient and accurate document clustering of large collections of documents remains a challenging task.  
     SUMMARY  
      It is an object of the invention to produce precise, meaningful clusters of similar documents.  
      It is another object of the invention to be able to cluster large collections of documents in a reasonable time.  
      It is another object of the invention to be able to generate a meaningful label, summary or other type of cluster content identifier.  
      According to one aspect, a method for identifying clusters of similar documents from among a set of documents comprises: (a) selecting a particular document based on rank from among a ranked set of documents; (b) generating a probe based on the particular document, the probe comprising one or more features; (c) finding documents that satisfy a similarity condition from among available documents of the set of documents using a search based upon the probe; and (d) associating some or all documents found with a particular cluster of documents. The method also comprises repeating steps (a)-(d) using another probe as the probe and using another similarity condition as the similarity condition until a halting condition is satisfied to identify at least one other cluster of documents. Those documents of the set of documents previously associated with a cluster of documents are not included among the available documents.  
      According to another aspect an apparatus comprises a memory and a processor coupled to the memory, wherein the processor is configured to execute the above-noted method.  
      According to another aspect, a computer readable carrier comprises processing instructions adapted to cause a processor to execute the above-noted method. 
    
    
     BRIEF DESCRIPTION OF THE FIGURES  
       FIG. 1  illustrates an exemplary flow diagram for identifying clusters of similar documents according to one aspect of the invention.  
       FIG. 2  illustrates another exemplary flow diagram for identifying clusters of similar documents according to one aspect of the invention.  
       FIG. 3  another exemplary flow diagram for identifying clusters of similar documents according to one aspect of the invention.  
       FIG. 4  illustrates an exemplary block diagram of a computer system on which exemplary approaches for identifying clusters of similar documents can be implement according to another aspect of the invention.  
    
    
     DETAILED DESCRIPTION  
       FIG. 1  illustrates an exemplary method  100  for identifying clusters of similar documents from among a set of documents. A cluster can be considered a collection of documents associated together based on a measure of similarity, and a cluster can also be considered a set of identifiers designating those documents. The exemplary method  100 , and other exemplary methods described herein, can be implemented using any suitable computer system comprising a processor and memory, such as will be described later in connection with  FIG. 4 .  
      A document as referred to herein includes text containing one or more strings of characters and/or other distinct features embodied in objects such as, but not limited to, images, graphics, hyperlinks, tables, charts, spreadsheets, or other types of visual, numeric or textual information. For example, strings of characters may form words, phrases, sentences, and paragraphs. The constructs contained in the documents are not limited to constructs or forms associated with any particular language. Exemplary features can include structural features, such as the number of fields or sections or paragraphs or tables in the document; physical features, such as the ratio of “white” to “dark” areas or the color patterns in an image of the document; annotation features, the presence or absence or the value of annotations recorded on the document in specific fields or as the result of human or machine processing; derived features, such as those resulting from transformation functions such as latent semantic analysis and combinations of other features; and many other features that may be apparent to ordinary practitioners in the art.  
      Also, a document for purposes of processing can be defined as a literal document (e.g., a full document) as made available to the system as a source document; sub-documents of arbitrary size; collections of sub-documents, whether derived from a single source document or many source documents, that are processed as a single entity (document); and collections or groups of documents, possibly mixed with sub-documents, that are processed as a single entity (document); and combinations of any of the above. A sub-document can be, for example, an individual paragraph, a predetermined number of lines of text, or other suitable portion of a full document. Discussions relating to sub-documents may be found, for example, in U.S. Pat. Nos. 5,907,840 and 5,999,925, the entire contents of each of which are incorporated herein by reference.  
      In the example of  FIG. 1 , a particular document (referred to as “doc S” for convenience) is selected based on rank from a ranked set of documents at step  102 . The ranked set of documents (e.g., a ranked list) can be obtained in any suitable way. For example, the ranked set could be generated from any suitable query over any source of documents that generates scores for responsive documents. The ranked set of documents can be chosen, for example, from the set of documents from which clusters will be generated, e.g., based upon any suitable query over the set of documents, or could be chosen from a source of documents other than the set of documents to be clustered (such that the ranked set of documents is not among the set of documents to be clustered). The query could be carried out over a single database or multiple databases, and could be carried out over distributed sources of documents such as via the Internet using any suitable search engine. Alternatively, the ranked set could be a set of documents hand-picked by a person and ranked in some order of preference from most relevant to least relevant. The particular document S could be selected as the highest ranking of those documents, or from another position in the ranked order (e.g., from a predetermined score range centered at or above the mean), for example.  
      At step  104 , a probe P is generated based on the particular document S. The probe can comprise one or more features and can be generated in any suitable manner. For example, the probe can comprise the document S itself, e.g., the terms from the text of the document S, possibly combined with any other features of the document S such as described elsewhere herein. As another example, the probe can comprise a subset of features selected from the particular document S, such as a weighted (or non-weighted) combination of features (e.g., terms) of the particular document S. As another example, the probe can comprise a subset of features selected from multiple documents (including the particular document S), such as a weighted (or non-weighted) combination of features (e.g., terms) of the multiple documents.  
      Also, as reflected at step  104  the probe P can optionally be formed based both the document S and based on a feature vector used to form the ranked set. A feature vector can be, for example, some or all of the features (e.g., terms) of a query used to generated the ranked set of documents. For example, the probe P could be a combination (e.g., a weighted combination) of some or all of the features (e.g., terms) of the document S and some or all of the features (e.g., terms) of the feature vector. It will be appreciated by ordinary practitioners in the art that many approaches could be used to form the probe P based on the document S and based on a feature vector used to form the ranked set.  
      As a general matter forming a suitable probe based on one or more documents can be accomplished by identifying features of the document(s), scoring the features, and selecting certain features (possibly all) based on the scores. Stated differently, probe formation can be viewed as a process that creates a probe P from a document set {D} (one or more documents) using a method M that specifies how to identify or features in documents and how to score or weight such terms or features, wherein the probe satisfies a test T that determines whether the probe should be formed at all and, if so, which features or terms the probe should include. Identifying distinct features of a document (or documents) and selecting all or a subset of such features for forming a probe is within the purview of ordinary practitioners in the art. For example, parsing document text to identify phrases of specified linguistic type (e.g., noun phrases), identifying structural features (such as the number of fields or sections or paragraphs or tables in the document), identifying physical features (such as the ratio of “white” to “dark” areas or the color patterns in an image of the document), identifying annotation features, including the presence or absence or the value of annotations, are all known in the art. Once such features are identified they can be scored using methods known in the art. One example is simply to count the number occurrences of a given identified feature, and to normalize each number of occurrences to the total number of occurrences of all identified features, and to set the normalized value to be the score of that feature. Depending upon the scores of the identified features, it may be decided not to form the probe at all based upon a given document or documents (e.g., because all of the scores or a combination of the scores fall below a threshold). Selection of a subset of features can be done, for example, by selecting those features that score above a given threshold (e.g., above the average score of the identified features) or by selecting a predetermined number (e.g., 10, 20, 50, 100, etc.) of highest scoring features. Other examples could be used as will be appreciated by ordinary practitioners in the art. Once the subset of features is selected, those features can be weighted, if desired, by renormalizing the number of occurrences a given feature to the total number of occurrences for the features of the subset, thereby providing a probe.  
      As suggested above, one exemplary subset of features (from one document or from multiple documents) to use as a probe can be a term profile of textual terms, such as described, for example, in U.S. Patent Application Publication No. 2004/0158569 to Evans et al., filed Nov. 14, 2003, the entire contents of which are incorporated herein by reference. One exemplary approach for generating a term profile is to parse the text and treat any phrase or word in a phrase of a specified linguistic type (e.g., noun phrase) as a feature. Such features or index terms can be assigned a weight by one of various alternative methods known to ordinary practitioners in the art. As an example, one method assigns to a term “t” a weight that reflects the observed frequency of t in a unit of text (“TF”) that was processed times the log of the inverse of the distribution count of t across all the available units that have been processed (“IDF”). Such a “TF-IDF” score can be computed using a document as a processing unit and the count of distribution based on the number of documents in a database in which term t occurs at least once. For any set of text (e.g., from one document or multiple documents) that might be used to provide features for a profile, the extracted features may derive their weights by using the observed statistics (e.g., frequency and distribution) in the given text itself. Alternatively, the weights on terms of the set of text may be based on statistics from a reference corpus of documents. In other words, instead of using the observed frequency and distribution counts from the given text, each feature in the set of text may have its frequency set to the frequency of the same feature in the reference corpus and its distribution count set to the distribution count of the same feature in the reference corpus. Alternatively, the statistics observed in the set of text may be used along with the statistics from the reference corpus in various combinations, such as using the observed frequency in the set of text, but taking the distribution count from the reference corpus. The final selection of features from example documents may be determined by a feature-scoring function that ranks the terms. Many possible scoring or term-selection functions might be used and are known to ordinary practitioners of the art. In one example, the following scoring function, derived from the familiar “Rocchio” scoring approach, can be used:  
         W   ⁡     (   t   )       =       IDF   ⁡     (   t   )       ⁢         ∑   D     ⁢       TF   D     ⁡     (   t   )           N   ⁢           ⁢   p             
 
      Here the score W(t) of a term “t” in a document set is a function of the inverse document frequency (IDF) of the term t in the set of documents (or sub-documents), or in a reference corpus, the frequency count TF D  of t in a given document D chosen for probe formation, and the total number of documents (or sub-documents) Np chosen to form the probe, where the sum is over all the documents (or sub-documents) chosen to form the probe. IDF is defined as
 
IDF( t )=log 2 ( N/n   t )+1
 
 where N is the count of documents in the set and n t  is the count of the documents (or sub-documents) in which t occurs. 
 
      Once scores have been assigned to features in the document set, the features can be ranked and all or a subset of the features can be chosen to use in the feature profile for the set. For example, a predetermined number (e.g., 10, 20, 50, 100, etc.) of features for the feature profile can be chosen in descending order of score such that the top-ranked terms are used for the feature profile.  
      Optionally, at step  105  an additional probe P′ can be generated based on the probe P and based on a feature vector used to form the ranked set of documents, e.g., such as described above For example, the additional probe P′ could be a combination (e.g., a weighted combination) of some or all of the features of the probe P and some or all of the features of the feature vector. The additional probe P′ could then be used as a query over the available documents. It will be appreciated that such a search is “based on” both probes since the additional probe P′ is based on the earlier probe P.  
      At step  106 , documents are found that satisfy a similarity condition from among available documents using a search based upon the probe (P, P′). Documents previously associated with a cluster of documents are not included among the available documents. For example, the probe itself (e.g., a profile of terms) could be used as a query over the available documents. The documents that satisfy the similarity condition can be referred to as “similar documents” for convenience. In this regard, a measure of the closeness or similarity between the probe and another document(s) (similarity score) can be generated using a suitable process (referred to as a similarity process for convenience), and the measure of closeness can be evaluated to determine whether it satisfies a similarity condition, e.g., meets or exceeds a predetermined threshold value. The threshold could be set at zero, if desired, i.e., such that documents that provide any non-zero similarity score are considered similar, or the threshold can be set at a higher value. As with other thresholds described herein generally, determining an appropriate threshold for a similarity score is within the purview of ordinary practitioners in the art and can be done, for example, by running the similarity process on sample or reference document sets to evaluate which thresholds produce acceptable results, by evaluating results obtained during execution of the similarity and making any needed adjustments (e.g., using feedback based on the number of similar documents identified is considered sufficient), or based on experience. As referred to herein, similarity can be viewed as a measure of the closeness or similarity between a reference document or probe and another document or probe. A similarity process can be viewed as a process that measures similarity of two vectors. In addition, the similarity scores of the responding documents can be normalized, e.g., to the similarity score of the highest scoring documents of the responding documents, and by other suitable methods that will be apparent to those of ordinary practitioners in the art.  
      It will be appreciated that the document S can be one of the available documents such that the document S can be among those “searched” using the probe at step  106 . Alternatively, since the probe is based, at least in part, on the document S, it is not necessary to include document S (if it is one of the available documents) in a search using the probe, since it can be assumed that document S will be one of the documents in the particular cluster that is formed. If document S is one of the available documents, both of these possibilities are intended to be embraced by the language herein “finding documents that satisfy a similarity condition using the probe from among the available documents” or similar language. Of course, as noted above, it is not necessary that the document S be one of the available documents.  
      Various methods for evaluating similarity between two vectors (e.g., a probe and a document) are known to ordinary practitioners in the art. In one example, described in U.S. Patent Application Publication No. 2004/0158569, a vector-space-type scoring approach may be used. In a vector-space-type scoring approach, a score is generated by comparing the similarity between a profile (or query) Q and the document D and evaluating their shared and disjoint terms over an orthogonal space of all terms. Such a profile is analogous to a probe referred to above. For example, the similarities score can be computed by the following formula (though many alternative similarity functions might also be used, which are known in the art):  
         S   ⁡     (       Q   i     ,     D   j       )       =           Q   i     ·     D   j                Q   i          ·          D   j              =         ∑     k   =   1     t     ⁢     (       q   ik     ·     d   jk       )               ∑     k   =   1     t     ⁢     q   ik   2         ·         ∑     k   =   1     t     ⁢     d   jk   2                   
 
 where Q i  refers to terms in the profile and D j  refers to terms in the document. Evaluating the expression above (or like expressions known in the art) provides a numerical measure of similarity (e.g., expressed as a decimal fraction). Then, as noted above, such a measure of similarity can be evaluated to determine whether it satisfies a similarity condition, e.g., meets or exceeds a predetermined threshold value. Thus, it will be appreciated that the similar documents found at step  206  can have scores that allow them to be ranked in terms of similarity to the probe P. 
 
      At step  108 , some or all of the documents that satisfy the similarity condition (similar documents) are associated with a particular cluster of documents. The association can be done, for example, by recording the status of the documents that satisfy the similarity condition in the same database that stores the set of documents, or in a different database, using, for example, appropriate pointers, marks, flags or other suitable indicators. For example, a list of the titles and/or suitable identification codes for the set documents can be stored in any suitable manner (e.g., a list), and an appropriate field in the database can be marked for a given document identifying the cluster to which it belongs, e.g., identified by cluster number and/or a suitable descriptive title or label for the cluster. The documents of the cluster could also be recorded in their own list in the database, if desired. It will be appreciated that it is not necessary to record or store all of the contents of the documents themselves for purposes of association with the cluster; rather, the information used to associate certain documents with certain clusters can contain a suitable identifier that identifies a given document itself as well as the cluster to which it is associated, for example. It is possible that the particular cluster may contain only the similar documents, or it is possible that the particular cluster may also contain additional documents beyond the similar documents (e.g., if it was known that at least some other documents should be associated with the cluster prior to initiating the method  100 ). This aspect is applicable for clusters identified by any of the exemplary approaches disclosed herein.  
      As noted above, just some as opposed to all of the similar documents identified at step  106  can be associated with a cluster at step  108 . Identifying some, as opposed to all of the similar documents, can be accomplished using a variety of approaches. For example, a predetermined percentage of the top scoring similar documents may be identified (e.g., top 80%, top 70%, top 60%, top 50%, top 40%, top 30%, top 20%, etc.), wherein it will be appreciated that the scores of the similar documents can be determined at step  106 . As another example, fewer than all similar documents could be selected by imposing another more stringent similarity condition (e.g., a higher threshold than that referred to in step  106 ). Also, if a “cluster boundary” the similar documents is generated, e.g., by defining the boundary to be a function of cluster quality (similarity of documents within a cluster) and specified desired cluster precision, then only the similar documents within the boundary can be selected to be in the cluster. It will be appreciated that other approaches for identifying fewer than all of the similar document for association with a cluster can also be used.  
      At step  110 , it is determined whether a halting condition is satisfied. For example, the method  100  could be halted after the entire set of documents is clustered, after a predetermined number of clusters has been created, after a predetermined percentage of the documents in the set of documents has been clustered, after a predetermined number of clusters of a minimum predetermined size has been created, or after a predetermined time interval has been exceeded. Other conditions can also be used as will be appreciated by ordinary practitioners in the art. If the halting condition is not satisfied (i.e., clustering should continue), steps  102 - 108  are repeated to form at least one other cluster. In this regard, another probe is generated from a different document S, and another similarity condition is utilized to find similar documents for a new cluster. The other similarity condition of the next iteration can be the same as the previous similarity condition, or it can be different from the previous similarity condition. It can be desirable to change (e.g., raise or lower) the similarity condition as iterations proceed to compensate for the removal of documents associated with previous iterations of clustering. At each iteration of cluster formation, the status of which documents are “available” can be updated so that documents associated with a cluster are no longer considered available documents. If documents of the set of ranked documents are among the documents of the set of documents being clustered, any documents associated with a cluster can be removed from the ranked set of documents. If the documents of the ranked set are not among the set of documents, the document S can be marked “used” such that it is not selected from the ranked set in another iteration of cluster formation. Optionally, even if the ranked set of documents is not among the set of documents being clustered, a given document S of the ranked set from which a cluster is generated can be added to that cluster.  
      If desired, similar documents of a given cluster can be ranked (e.g., listed in ranked order in a database) as the given cluster is identified. Finding the similar documents using methods that generate scores or weights, such as discussed above, can automatically provide ranking information. Also, the method  100  can comprise providing an identifier (referred to as a “content identifier” for convenience) that describes the content of a given cluster. For example, the title of the highest ranking document of a given cluster could be used as the content identifier. As another example, all or some terms (or description of features) of the probe could be used as the content identifier, or all or some terms of a new probe generated from multiple close documents that satisfy another similarity condition could be used as the content identifier. These aspects apply to the other exemplary methods disclosed herein as well.  
      As noted above, the document S is selected from a ranked set of documents, and various ways of generating such a ranked set can be used, including those mentioned above and others that will be apparent to ordinary practitioners in the art. Another exemplary method for generating such a ranked set (e.g., a ranked list) can be based upon multiple queries over the set of documents. In particular, for all or some of the documents in the set of documents, a query can be executed using a probe formed from that document over the set of documents, yielding a list of responsive documents ranked according to their similarity scores. For each set of responsive documents, a collective score of the responsive documents can be generated, e.g., by summing the scores of each responsive document, or by calculating the average response score, etc. This collective score can then be associated with the particular document whose probe produced a given set of responsive documents. Those collective scores can then be ranked and normalized against the highest collective score. Then, those documents with associated collective scores above a predetermined threshold can be selected as the set of ranked documents from which to form clusters of documents, wherein individual documents S can be selected from the ranked set of documents beginning with the highest ranking of the ranked set of documents and proceeding to lower ranking candidate documents.  
      According to another aspect of the invention,  FIG. 2  illustrates an exemplary method  200  for identifying clusters of similar documents. Steps  202 - 206  are analogous to steps  102 - 106  previously described, and these steps do not require further discussion. At the point of step  206 , a set of similar documents has been identified using a probe (P or P′, where P′ is based upon P).  
      From step  206 , the process proceeds to step  212 , where a new probe is formed based on close documents of the similar documents (a subset of the similar documents), which will typically include the document S. Any of the exemplary approaches previously described herein for forming probes (or other suitable approach) can be used at step  212 . The “close documents” (a label used for convenience herein) used in forming the new probe P can be those documents of the similar documents (found at step  206 ) that satisfy another similarity condition (e.g., a more stringent threshold than that used in identifying the similar documents, a predetermined number or percentage of the top ranking similar documents, etc.). Since the similar documents found at step  206  can already have rank scores, the close documents can simply be designated as such in view of those scores. In other words, a separate query or other type of search is not necessary to identify the close documents.  
      At step  214 , documents are found using the new P that satisfy a similarity condition from among the available documents. These documents can be referred to as “new similar documents” for convenience to avoid confusion with the “similar documents” found at step  206 , considering that the new similar documents are found using a new probe P. Step  214  can be carried out as described in connection with steps  106  and  206  of  FIGS. 1 and 2 , for example.  
      As is true with the other exemplary methods described herein, the similarity conditions at steps  206  and  214  can change as iterations of cluster formation proceed. For example, an initial value of the similarity condition at step  214  can be a function of the object density in the neighborhood of the document S and, optionally, a function of a specified minimum cluster size. The effect is to select a number of documents close to the probe. For example, the threshold of the similarity condition can be adjusted based on feedback (e.g., whether cluster formation is meeting expectations) or changed by predetermined amounts as a function of iteration. For example, as iterations proceed, it is possible to either raise or lower the similarity condition to compensate for the removal of similar documents as clustering proceeds. Raising the similarity condition might be done to achieve more precise clusters as clustering proceeds; lowering the similarity condition might be done to speed the process to completion after a certain number of clusters have been obtained or after a certain percentage of the set of documents has been clustered. It will also be appreciated that, although steps  206  and  214  each refer to a similarity condition, these similarity conditions may or may not be the same. These comments are applicable to other exemplary methods illustrated herein as well.  
      At step  208 , some or all of the similar documents identified at step  214  are associated with a cluster. This step can be carried out as discussed in connection with step  108  of  FIG. 1 . In addition to the ways discussed in connection with step  108  for identifying or selecting some, as opposed to all of the similar documents, any of the approaches described above for identifying “close documents” can be used to select some of the similar documents for association with a cluster.  
      At step  210 , a determination is made as to whether a halting condition is satisfied. This step is analogous to step  110  of  FIG. 1  and does not need to be described further. If the halting condition is satisfied (i.e., no more clustering is needed or desired), the process ends. If the halting condition is not satisfied, the process proceeds back to step  202  of  FIG. 2 , and steps  202 - 214  are repeated as discussed above.  
       FIG. 3  illustrates an exemplary method  300  for identifying clusters of similar documents. Steps  302 - 314  are analogous to steps  202 - 214  of  FIG. 2 , respectively, and no further discussion of those steps as an initial matter is needed.  FIG. 3  adds steps  316 - 326 .  
      At step  316 , the similarity scores of the new similar documents are recorded or updated as appropriate (e.g., saved/updated in a database, which can be the same database that maintains the clustering information relating to the set of documents, or a different database). These similarity scores can be provided by exemplary processes for finding the similar documents as previously discussed. Optionally, the new similar documents can be sorted according to their similarity scores. These documents can be referred to as “scored documents” for convenience, but it will be apparent that they are also considered the new similar documents, as discussed above. Considering the loop between steps  312  and  326 , a given document found as a similar document (or new similar document) could be scored multiple times. If a given document has already been scored and receives a new score in any iteration of the loop, the new score can be added or otherwise accumulated to the old score for that document, and the accumulated score associated with that document can be updated by recording the accumulated score.  
      At step  318 , it is determined whether a given set of scored documents, which are essentially a candidate cluster at this stage, satisfies a cluster condition. If the cluster condition is satisfied, the process proceeds to step  308  where some or all of the scored documents (new similar documents) are associated with a cluster, such as has been described previously herein. The process then continues step  310  to determine whether to halt clustering.  
      If the cluster condition at step  318  is not satisfied, the process proceeds from step  318  to step  320 . At step  320  a new document S is selected from the new similar documents identified at step  314  (e.g., the new document S can be the highest ranking of the new similar documents, or a document that satisfies another condition such as described elsewhere herein) as long as it is not marked “used,” meaning it has not been used previously to form a probe. At step  322 , a new probe P is formed based on the new document S using any suitable method for probe formation such as described herein. At step  324 , documents that satisfy a similarity condition are found using P from among the available documents, such as described elsewhere herein. At step  326 , the new document S is marked as “used” or is flagged in any other suitable manner to indicate that the document S has been previously used to form a probe so that it is not used again in a subsequent iteration of steps  312 - 326  (step  326  could occur at a different location in the ordering of steps). These resulting similar documents are then used as input to step  312 , i.e., they can be used as the “close docs” referred to at step  312 , or a subset of the similar documents found at step  324  can be used as the “close docs” in step  312 . At step  312 , a further new probe P is formed based upon the newly found similar or close documents from step  324 . Steps  312 - 318  are then executed as described above, and if the cluster condition is still not satisfied, the process will proceed again to steps  320 - 326  to provide input again to step  312 . The looping between steps  312 - 326  can be viewed as a process where the probe is iteratively refined using at least one new document (typically more than one) for forming the refined probe and where the emerging cluster is refined.  
      Any of a variety of cluster conditions can be utilized at step  318  in this process. For example, one cluster condition can be whether all of the documents of the emerging cluster (i.e., those found at step  314 ) have been used as the new document S at step  320 . If yes, the process proceeds to step  308 , and the looping through steps  312 - 326  terminates. As another example, the cluster condition can be whether the size of the emerging cluster has saturated after a predetermined number of iterations through the loop of steps  312 - 326  (e.g., N consecutive loops do no find new documents at step  314 ). As another example, the cluster condition can be whether a predetermined number of iterations through the loop of steps  312 - 326  has occurred. Other conditions can also be used as will be appreciated by ordinary practitioners in the art.  
      In addition, the similarity condition at step  314  can be changed such as described elsewhere herein (e.g., the threshold of the condition can be adjusted based on feedback or changed by predetermined amounts as a function of iteration of clustering operations). In addition, it can also be desirable to further adjust the similarity condition at step  314  in view of the probe/cluster refinement loop of steps  312 - 326 . In particular, it can be desirable to further adjust the similarity condition used at step  314  as a function of score profile of the scored documents in a given iteration of the probe/cluster refinement loop of steps  312 - 326  (e.g., a threshold for the cluster condition can be incremented by positive or negative amounts depending upon the score profile of the scored documents).  
      With regard to step  308 , various approaches have been previously described for selecting only some, as opposed to all of the scored or similar documents for inclusion in a cluster. One approach previously mentioned involves detecting documents at the “cluster boundary” of the scored documents (which can be considered an emerging cluster), and eliminating those documents such that they are not associated with the cluster. In the context of  FIG. 3 , documents at the cluster boundary can be identified, for example, as those documents seen in less than a certain percentage of cluster refining probe responses through iterations of steps  312 - 326 . These boundary documents can be eliminated so that they are not associated with the cluster.  
      Exemplary methods described herein can have notable advantages compared to known clustering approaches. For example, if random selection is used to choose a document from which to generate a probe for clustering, the most coherent and largest clusters tend to be generated first because the randomly selected document is likely a member of one of the larger thematic groups of the set of documents. If a seed list is established, selecting the highest (or a highly ranking) seed document from which to generate a probe also tends to generate the largest and most coherent clusters first. For each cluster, the methods described herein can rank documents according to their importance to the cluster. Meaningful labels or identifiers of cluster content for a given cluster can be generated from terms or descriptions of features from the probe that created the cluster. The exemplary methods do not require processing the entire set of documents to achieve final clusters; rather, final, complete clusters are generated during each iteration of cluster formation. Thus, even if the process is aborted prematurely, final results for what are likely the most important clusters can be obtained. The methods are computationally efficient and fast because each cluster is removed in a single pass, leaving fewer documents to process during the next iteration of cluster formation.  
     HARDWARE OVERVIEW  
       FIG. 4  illustrates a block diagram of an exemplary computer system upon which an embodiment of the invention may be implemented. Computer system  1300  includes a bus  1302  or other communication mechanism for communicating information, and a processor  1304  coupled with bus  1302  for processing information. Computer system  1300  also includes a main memory  1306 , such as a random access memory (RAM) or other dynamic storage device, coupled to bus  1302  for storing information and instructions to be executed by processor  1304 . Main memory  1306  also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor  1304 . Computer system  1300  further includes a read only memory (ROM)  1308  or other static storage device coupled to bus  1302  for storing static information and instructions for processor  1304 . A storage device  1310 , such as a magnetic disk or optical disk, is provided and coupled to bus  1302  for storing information and instructions.  
      Computer system  1300  may be coupled via bus  1302  to a display  1312  for displaying information to a computer user. An input device  1314 , including alphanumeric and other keys, is coupled to bus  1302  for communicating information and command selections to processor  1304 . Another type of user input device is cursor control  1315 , such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor  1304  and for controlling cursor movement on display  1312 .  
      The exemplary methods described herein can be implemented with computer system  1300  for carrying out document clustering. The clustering process can be carried out by processor  1304  by executing sequences of instructions and by suitably communicating with one or more memory or storage devices such as memory  1306  and/or storage device  1310  where the set of documents and clustering information relating thereto can be stored and retrieved, e.g., in any suitable database. The processing instructions may be read into main memory  1306  from another computer-readable carrier, such as storage device  1310 . However, the computer-readable carrier is not limited to devices such as storage device  1310 . For example, the computer-readable carrier may include a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read, including any modulated waves/signals (such as radio frequency, audio frequency, or optical frequency modulated waves/signals) containing an appropriate set of computer instructions that would cause the processor  1304  to carry out the techniques described herein. Execution of the sequences of instructions causes processor  1304  to perform process steps previously described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the exemplary methods described herein. Thus, embodiments of the invention are not limited to any specific combination of hardware circuitry and software.  
      Computer system  1300  can also include a communication interface  1316  coupled to bus  1302 . Communication interface  1316  provides a two-way data communication coupling to a network link  1320  that is connected to a local network  1322  and the Internet  1328 . It will be appreciated that the set of documents to be clustered can be communicated between the Internet  1328  and the computer system  1300  via the network link  1320 , wherein the documents to be clustered can be obtained from one source or multiples sources. Communication interface  1316  may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface  1316  may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface  1316  sends and receives electrical, electromagnetic or optical signals which carry digital data streams representing various types of information.  
      Network link  1320  typically provides data communication through one or more networks to other data devices. For example, network link  1320  may provide a connection through local network  1322  to a host computer  1324  or to data equipment operated by an Internet Service Provider (ISP)  1326 . ISP  1326  in turn provides data communication services through the “Internet”  1328 . Local network  1322  and Internet  1328  both use electrical, electromagnetic or optical signals which carry digital data streams. The signals through the various networks and the signals on network link  1320  and through communication interface  1316 , which carry the digital data to and from computer system  1300 , are exemplary forms of modulated waves transporting the information.  
      Computer system  1300  can send messages and receive data, including program code, through the network(s), network link  1320  and communication interface  1316 . In the Internet  1328  for example, a server  1330  might transmit a requested code for an application program through Internet  1328 , ISP  1326 , local network  1322  and communication interface  1316 . In accordance with the invention, one such downloadable application can provides for carrying out document clustering as described herein. Program code received over a network may be executed by processor  1304  as it is received, and/or stored in storage device  1310 , or other non-volatile storage for later execution. In this manner, computer system  1300  may obtain application code in the form of a modulated wave, which is intended to be embraced within the scope of a computer-readable carrier.  
      Components of the invention may be stored in memory or on disks in a plurality of locations in whole or in part and may be accessed synchronously or asynchronously by an application and, if in constituent form, reconstituted in memory to provide the information required for retrieval or filtering of documents.  
      While this invention has been particularly described and illustrated with reference to particular embodiments thereof, it will be understood by those skilled in the art that changes in the above description or illustrations may be made with respect to form or detail without departing from the spirit or scope of the invention. For example, while flow diagrams of the figures herein show process steps occurring in exemplary orders, it will be appreciated that all steps do not necessarily need to occur in the orders illustrated.