A New Algorithm for Clustering in Single-step based on an Information-theoretic Mutual Irrelevance Metric

A new algorithm that can extract clusters in single-step based on a new information-theoretic notion is described. New method employs similarity-based sample entropy and probability descriptions to express scatter in a given dataset. Based on these quantities, a new information-theoretic association measure called mutual irrelevance metric is defined to model a (dis)-connectivity rule between samples. This metric is utilized for determining candidate cluster representative samples coined cluster indicators. Possible clusters are established based on an association quantity between samples and cluster indicators in a single iteration. Clustering capability of new approach is demonstrated for a non-convex dataset, which is hard to cluster by using most well known counterparts. It is also tested and compared to major algorithms for publicly available real datasets. Experimental results reveal that the proposed approach outperforms predecessors it is compared to.