17. A computerized method of analyzing a plurality of documents, comprising: collecting and filtering terms from a plurality of documents; identifying a term-frequency vector for each of the documents; identifying a term-frequency matrix using the term-frequency vector, wherein rows of the matrix comprise values for the term-frequency vectors; projecting the term-frequency matrix onto a lower dimensional space using latent semantic analysis, to create a transformed term matrix; developing a correlation matrix comprising columns and rows corresponding, to terms of the transformed term matrix, and a plurality of elements each having a correlation value indicating a statistical relationship exclusively between two of the terms; creating a dendrogram of related concepts using a function of the correlation matrix; identifying branches of the dendrogram corresponding to related concepts; and clustering documents that contain concept term sets together.
17. A computerized method of analyzing a plurality of documents, comprising: collecting and filtering terms from a plurality of documents; identifying a term-frequency vector for each of the documents; identifying a term-frequency matrix using the term-frequency vector, wherein rows of the matrix comprise values for the term-frequency vectors; projecting the term-frequency matrix onto a lower dimensional space using latent semantic analysis, to create a transformed term matrix; developing a correlation matrix comprising columns and rows corresponding, to terms of the transformed term matrix, and a plurality of elements each having a correlation value indicating a statistical relationship exclusively between two of the terms; creating a dendrogram of related concepts using a function of the correlation matrix; identifying branches of the dendrogram corresponding to related concepts; and clustering documents that contain concept term sets together. 18. The method of claim 17 , wherein the filtering is performed with reference to a set of stop words.