1. A system for scoring concepts in a document set, comprising: a database to maintain a set of documents; a concept identification module to identify concepts comprising two or more terms extracted from the document set and to designate each document having one or more of the concepts as a candidate seed document; a value module to determine for each of the concepts identified within each candidate seed document, values for a frequency of occurrence of that concept within that candidate seed document, a concept weight reflecting a specificity of meaning for that concept within that candidate seed document, a structural weight reflecting a degree of significance based on a location of that concept within that candidate seed document, and a corpus weight inversely weighing a reference count of the occurrence for that concept within the document set according to the equation: r ⁢ ⁢ w ij = { ( T - r ij T ) 2 , r ij > M 1.0 , r ij ≤ M where rw ij comprises the corpus weight, r ij comprises the reference count for the occurrence j for that concept i, T comprises a total number of reference counts of the documents in the document set, and M comprises a maximum reference count of the documents in the document set; a scoring module to calculate a score for each concept as a function of summation of the values for each of the frequency of occurrence, concept weight, structural weight, and corpus weight; a vector module to form a vector for each candidate seed document comprising the concepts located in that candidate seed document and the associated concept scores based on the frequency of occurrence, concept weight, structural weight, and corpus weight; a document comparison module to compare the vector for each candidate seed document with a center of one or more clusters each comprising thematically-related documents and to select at least one of the candidate seed documents that is sufficiently distinct from the other candidate seed documents as a seed document for a new cluster; and a clustering module to place each of the unselected candidate seed documents into one of the clusters having a most similar cluster center.
1. A system for scoring concepts in a document set, comprising: a database to maintain a set of documents; a concept identification module to identify concepts comprising two or more terms extracted from the document set and to designate each document having one or more of the concepts as a candidate seed document; a value module to determine for each of the concepts identified within each candidate seed document, values for a frequency of occurrence of that concept within that candidate seed document, a concept weight reflecting a specificity of meaning for that concept within that candidate seed document, a structural weight reflecting a degree of significance based on a location of that concept within that candidate seed document, and a corpus weight inversely weighing a reference count of the occurrence for that concept within the document set according to the equation: r ⁢ ⁢ w ij = { ( T - r ij T ) 2 , r ij > M 1.0 , r ij ≤ M where rw ij comprises the corpus weight, r ij comprises the reference count for the occurrence j for that concept i, T comprises a total number of reference counts of the documents in the document set, and M comprises a maximum reference count of the documents in the document set; a scoring module to calculate a score for each concept as a function of summation of the values for each of the frequency of occurrence, concept weight, structural weight, and corpus weight; a vector module to form a vector for each candidate seed document comprising the concepts located in that candidate seed document and the associated concept scores based on the frequency of occurrence, concept weight, structural weight, and corpus weight; a document comparison module to compare the vector for each candidate seed document with a center of one or more clusters each comprising thematically-related documents and to select at least one of the candidate seed documents that is sufficiently distinct from the other candidate seed documents as a seed document for a new cluster; and a clustering module to place each of the unselected candidate seed documents into one of the clusters having a most similar cluster center. 8. A system according to claim 1 , further comprising: a score compression module to compress the concept scores.