1. A computer-implemented system for clustering similar documents, comprising: concepts for a set of documents; an occurrence module to determine occurrence frequencies of each concept in the document set; a distance module to calculate an inner product quantifying a similarity for each of the documents in the set with one or more clusters of documents based on the occurrence frequencies of the concepts; a map module to map each document to each of the document clusters based on the inner product, to identify those documents with the smallest inner products as most relevant to a theme, and to generate a matrix as a representation of the document and cluster mappings; and a processor to execute the modules.
1. A computer-implemented system for clustering similar documents, comprising: concepts for a set of documents; an occurrence module to determine occurrence frequencies of each concept in the document set; a distance module to calculate an inner product quantifying a similarity for each of the documents in the set with one or more clusters of documents based on the occurrence frequencies of the concepts; a map module to map each document to each of the document clusters based on the inner product, to identify those documents with the smallest inner products as most relevant to a theme, and to generate a matrix as a representation of the document and cluster mappings; and a processor to execute the modules. 5. A system according to claim 1 , further comprising: a lexicon generation module to build a lexicon of the concepts by mapping individual occurrences of each concept within one or more of the documents.