SYMBOLIC CLUSTERING USING A NEW DISSIMILARITY MEASURE

A new dissimilarity measure, based on "position", "span" and "content" of symbolic objects is proposed for symbolic clustering. The dissimilarity measure is new in the sense that it is not just another aspect of a similarity measure. In the proposed hierarchical agglomerative clustering methodology, composite symbolic objects are formed using a Cartesian join operator whenever a mutual pair of symbolic objects is selected for agglomeration based on minimum dissimilarity. The minimum dissimilarity values of different merging levels are used to compute the cluster indicator values and hence to determine the number of clusters in the data. The results of the application of the algorithm on numeric data of known number of classes are described first so as to show the efficacy of the method. Subsequently, the results of the experiments on two data sets of Assertion type of symbolic objects drawn from the domains of fat-oil and microcomputers are presented.