Relational clustering based on a dissimilarity relation extracted from data by a TS model

Most clustering algorithms partition a data set based on a dissimilarity relation expressed in terms of some distance function. When the nature of this relation is conceptual rather than metric, distance functions may fail to adequately model dissimilarity. For this reason, we propose to extract dissimilarity relations directly from the data. We exploit some pairs of patterns with known dissimilarity to build a TS fuzzy system which models the dissimilarity relation. Then, we use the TS system to compute a dissimilarity relation between any pair of patterns. The resulting dissimilarity matrix is input to a new unsupervised fuzzy relational clustering algorithm, which partitions the data set based on the proximity of the vectors containing the dissimilarity values between a pattern and all the patterns in the data set. Experimental results to confirm the validity of our approach are shown and discussed.