On Some Fuzzy Clustering Algorithms with Dimensionality Reduction
Masanori Kawamura, Yuchi Kanzawa · 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · 2022
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.