An analysis of partition index maximization algorithm

Abstract

In the traditional fuzzy c-means clustering algorithm, nearly no data points have a membership value one. Özdemir and Akarum proposed a partition index maximization (PIM) algorithm which allows the data points can whole belonging to one cluster. This modification can form a core for each cluster and data points inside the core will have membership value {0,1}. In this paper, we will discuss the parameter selection problems and robust properties of the PIM algorithm.