Analyzing Distance Measures for Symbolic Data Based on Fuzzy Clustering

Various propositions to solve the problem of symbolic data clustering are available in the literature. This paper introduces a comparative study among some well known dissimilarity functions treating symbolic data. An extension of the fuzzy c-means clustering algorithm is used to create groups of individuals characterized by symbolic variables of mixed types. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion dependent on the dissimilarity function. Experiments involving benchmark data sets are carried out in order to compare the accuracy of each function.