18. A document clustering system for assigning a document to one of a plurality of clusters of documents containing related content, the document having at least one feature, each cluster being associated with a cluster summary describing the content of the documents in the cluster and comprising a summary of one or more features of the documents in the cluster, wherein the summary comprises an aggregated feature vector that describes the set of documents summarized, the document clustering system comprising: a memory for storing one or more previously created cluster summaries, the previously created cluster summaries being stored in the memory in a B-tree data structure; one or more processors, configured to: determine whether the document should be grouped with one or more of the previously created cluster summaries by comparing the at least one feature of the document with each previously created cluster summary and evaluating the similarity therebetween; determine that the document should not be grouped with the one or more previously created cluster summaries, and create a cluster summary based on the content of the document and store the created cluster summary in the B-tree data structure, wherein the leaf nodes of the B-tree store the cluster summaries; identify at least one outlier in the plurality of cluster summaries associated with each of the plurality of clusters; attempt to merge the at least one outlier into other cluster summaries; increment a merge attempt count each time a merge is attempted; and remove the outlier from the B-tree data structure when the merge attempt count exceeds a predetermined threshold.
18. A document clustering system for assigning a document to one of a plurality of clusters of documents containing related content, the document having at least one feature, each cluster being associated with a cluster summary describing the content of the documents in the cluster and comprising a summary of one or more features of the documents in the cluster, wherein the summary comprises an aggregated feature vector that describes the set of documents summarized, the document clustering system comprising: a memory for storing one or more previously created cluster summaries, the previously created cluster summaries being stored in the memory in a B-tree data structure; one or more processors, configured to: determine whether the document should be grouped with one or more of the previously created cluster summaries by comparing the at least one feature of the document with each previously created cluster summary and evaluating the similarity therebetween; determine that the document should not be grouped with the one or more previously created cluster summaries, and create a cluster summary based on the content of the document and store the created cluster summary in the B-tree data structure, wherein the leaf nodes of the B-tree store the cluster summaries; identify at least one outlier in the plurality of cluster summaries associated with each of the plurality of clusters; attempt to merge the at least one outlier into other cluster summaries; increment a merge attempt count each time a merge is attempted; and remove the outlier from the B-tree data structure when the merge attempt count exceeds a predetermined threshold. 19. The document clustering system of claim 18 , wherein determining whether the document should be grouped with the one or more previously created cluster summaries comprises: creating a feature vector for the document, the feature vector representing content of the documents; comparing the feature vector with the one or more previously created cluster summaries to identify the cluster summary that is closest to the feature vector and determining a measure of similarities between the cluster summary and the feature vector; determining whether the cluster summary that was identified as the closest to the feature vector is sufficiently close to the feature vector by comparing the measure of the similarities with one or more predetermined thresholds; and if the cluster summary that was identified as the closest to the feature vector is not sufficiently close, then determining that the document should not be grouped with the one or more previously created cluster summaries.