查看文章

In this paper, a novel similarity measure for estimating the degree of similarity between two patterns (described by interval type data) is proposed. The proposed measure computes the degree of similarity between two patterns and approximates the computed similarity value by a multivalued type data. Unlike conventional proximity matrices, the proximity matrix obtained through the application of the proposed similarity measure is not necessarily symmetric. Based on this unconventional similarity matrix a modified agglomerative method by introducing the concept of mutual similarity value (MSV) for clustering symbolic patterns is also presented. Experiments on various data sets have been conducted in order to study the efficacy of the proposed methodology.