On the Distribution of Dissimilarity Increments

摘要

This paper proposes a statistical model for the dissimilarity changes (increments) between neighboring patterns which follow a 2-dimensional Gaussian distribution. We propose a novel clustering algorithm, using that statistical model, which automatically determines the appropriate number of clusters. We apply the algorithm to both synthetic and real data sets and compare it to a Gaussian mixture and to a previous algorithm which also used dissimilarity increments. Experimental results show that this new approach yields better results than the other two algorithms in most datasets.