A New Validity Index for Fuzzy-Possibilistic C-Means Clustering

TL;DR

This paper introduces a new validity index for fuzzy-possibilistic c-means clustering that effectively handles noisy data, outliers, and varying cluster shapes, with an efficient method for parameter selection, validated on synthetic, real-world, and biomedical datasets.

Contribution

The paper proposes the FP index for improved cluster validity in complex datasets and an efficient procedure for optimal parameter determination in fuzzy-possibilistic clustering.

Findings

The FP index outperforms existing validity indices in noisy and complex datasets.

The proposed parameter selection method enhances clustering accuracy.

Successful application in gene expression and medical image segmentation.

Abstract

In some complicated datasets, due to the presence of noisy data points and outliers, cluster validity indices can give conflicting results in determining the optimal number of clusters. This paper presents a new validity index for fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic (FP) index, which works well in the presence of clusters that vary in shape and density. Moreover, FPCM like most of the clustering algorithms is susceptible to some initial parameters. In this regard, in addition to the number of clusters, FPCM requires a priori selection of the degree of fuzziness and the degree of typicality. Therefore, we presented an efficient procedure for determining their optimal values. The proposed approach has been evaluated using several synthetic and real-world datasets. Final computational results demonstrate the capabilities and reliability of the proposed approach compared with several well-known fuzzy validity indices in the literature. Furthermore, to clarify the ability of the proposed method in real applications, the proposed method is implemented in microarray gene expression data clustering and medical image segmentation.