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On Some Fuzzy Clustering Algorithms with Dimensionality Reduction | IEEE Conference Publication | IEEE Xplore

On Some Fuzzy Clustering Algorithms with Dimensionality Reduction


Abstract:

Certain fuzzy clustering algorithms involve dimensionality reduction techniques, such as principal component analysis (PCA), probabilistic principal component analysis (P...Show More

Abstract:

Certain fuzzy clustering algorithms involve dimensionality reduction techniques, such as principal component analysis (PCA), probabilistic principal component analysis (PPCA), and t-factor analysis (t-FA). Other fuzzification techniques have been applied to fuzzy clustering without dimensionality reduction. In this study, eleven fuzzy clustering algorithms are proposed based on five dimensionality reduction methods: PCA, PPCA, t-distribution-based PPCA, FA, and t-FA; and three fuzzification techniques: Bezdek-type, Kullback-Leibler divergence-regularization, and q-divergence-regularization. Based on numerical experiments using an artificial dataset, it is shown that some of the proposed methods outperforms the conventional methods on clustering accuracy.
Date of Conference: 29 November 2022 - 02 December 2022
Date Added to IEEE Xplore: 04 January 2023
ISBN Information:
Conference Location: Ise, Japan

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