Interval Type-2 Enhanced Possibilistic Fuzzy C-Means Clustering for Gene Expression Data Analysis

TL;DR

This paper introduces an advanced clustering algorithm, IT2EPFCM, that improves gene expression data analysis by addressing noise sensitivity and cluster coincidence issues in existing fuzzy clustering methods.

Contribution

The paper proposes the IT2EPFCM algorithm, combining interval type-2 fuzzy logic with enhanced possibilistic clustering, improving robustness and accuracy over previous methods.

Findings

IT2EPFCM outperforms state-of-the-art clustering techniques.

The method effectively analyzes microarray gene expression data.

Enhanced clustering accuracy demonstrated on benchmark datasets.

Abstract

Both FCM and PCM clustering methods have been widely applied to pattern recognition and data clustering. Nevertheless, FCM is sensitive to noise and PCM occasionally generates coincident clusters. PFCM is an extension of the PCM model by combining FCM and PCM, but this method still suffers from the weaknesses of PCM and FCM. In the current paper, the weaknesses of the PFCM algorithm are corrected and the enhanced possibilistic fuzzy c-means (EPFCM) clustering algorithm is presented. EPFCM can still be sensitive to noise. Therefore, we propose an interval type-2 enhanced possibilistic fuzzy c-means (IT2EPFCM) clustering method by utilizing two fuzzifiers $(m_1, m_2)$ for fuzzy memberships and two fuzzifiers $({\theta}_1, {\theta}_2)$ for possibilistic typicalities. Our computational results show the superiority of the proposed approaches compared with several state-of-the-art techniques in the literature. Finally, the proposed methods are implemented for analyzing microarray gene expression data.