Agglomerative Hierarchical Clustering Based on Probabilistic Similarity

Abstract

In clustering, data are classified based on similarity which is a measure of closeness between a part of data. Similarity is often expressed as a value in the interval [0, 1] rather than as a discrete value represented by {0, 1}, because it is expected to provide fine-grained classification, high versatility, technological development, and theoretical depth. In particular, probabilistic similarity is theoretically interesting because it is a measure that evaluates similarity not by its own value but by the probability that the similarity takes a predefined value. However, clustering and pattern classification methods based on probabilistic similarity have not been discussed much. In this paper, we propose a new hierarchical clustering algorithm based on probabilistic similarity and verify its effectiveness.