Deriving term relations for a corpus by graph theoretical clusters
J. Gary Augustson, Jack Minker · Journal of the American Society for Information Science · 1970
Abstract We discuss how alternative methods of automatic term clustering may provide insight into how terms are related within a corpus. The work reported uses a corpus of 2267 documents that contain 3950 index terms. A similarity matrix is developed using the document–term matrix. A threshold level T is applied to the similarity matrix. Entries in the matrix that are greater than or equal to the threshold level are set equal to one, and the remaining entries are set to zero. Three definitions are applied to the corresponding graph of each threshold matrix to develop clusters. These are, (1) the connected components of the graph, (2) the maximal complete subgraphs of the graph, and (3) the combined maximal complete subgraphs of the graph as described by Gotlieb and Kumar. Two examples are described that show how insight may be gained into the term relations by varying the threshold levels and the cluster definitions.