A self-learning iterative weighted possibilistic fuzzy c-means clustering via adaptive fusion

Abstract

Considering that weighted possibilistic fuzzy clustering does not obtain significant performance compared with possibilistic fuzzy clustering, so this paper proposes an enhanced self-adaptive weighted possibilistic fuzzy clustering algorithm. Firstly, the principle of maximum entropy is introduced to weighted possibilistic fuzzy clustering, and the weighted coefficients of fuzzy clustering and possibilistic clustering are subject to regularization entropy constraint and a novel self-learning iterative weighted possibilistic fuzzy clustering is obtained, and its convergence is strictly proved by Zangwill theorem and bordered Hessian matrix. Secondly, a series of clustering validity functions for the proposed algorithm are constructed to determine the optimal number of clusters in the data set. In the end, to enhance the anti-noise robustness of the proposed algorithm, a robust loss function is applied in the adaptive weighted possibilistic fuzzy clustering, and a robust algorithm is obtained for noisy data clustering. Experimental results show that the proposed algorithm outperforms existing possibilistic fuzzy clustering-related algorithms, and the validity functions for the proposed algorithm can accurately determine the optimal number of clusters in the data set, meanwhile, the corresponding robust algorithm effectively enhances the performance of the algorithm in the presence of noise.