Parameter independent clustering based on dominant sets and cluster merging

Abstract

Clustering is an important unsupervised learning approach with wide application in data mining, pattern recognition and intelligent information processing. However, existing clustering algorithms usually involve one or more user-specified parameters as input and their clustering results depend heavily on these parameters. In order to solve this problem, we present a parameter independent clustering algorithm based on the dominant sets algorithm and cluster merging. In the first step histogram equalization transformation is applied to solve the parameter dependence problem of the dominant sets algorithm. We provide the theoretic foundation of this method and discuss the implementation details. The clustering result is then refined with a cluster merging method, which is based on a new clustering quality evaluation criterion. We use extensive experiments on several datasets to validate each step and the whole procedures of our algorithm. It is shown that our parameter independent algorithm performs comparably to some existing clustering algorithms which benefit from user-specified parameters.