On Variants of Fuzzy Clustering Algorithm with Dimensionality Reduction

Abstract

In many variants of the fuzzy c-means (FCM) clustering algorithms, two variants are focused in this work: Yang’s FCM (YFCM) and the extended q-divergence-based FCM (EQFCM). These pro-(breakpoint)cedures of fuzzification have not been utilized in conjunction with certain fuzzy clustering algorithms that incorporate any dimension reduction methods. proposed, based on two procedures of fuzzification: Yang-type and extended q-divergence regularization, In this study, ten fuzzy clustering algorithms are along with five types of dimension reduction methods: principal component analysis (PCA), probabilistic PCA (PPCA), t-distribution-based PPCA, factor analysis (FA), and t-distribution-based FA. Numerical experiments conducted on one artificial dataset and two real datasets demonstrate that the combination of extended q-divergence regularization and t-FA outperforms the others, including conventional methods, in terms of clustering accuracy.