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

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 t...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Expert systems with applications Ročník 209; s. 118280
Hlavní autoři: Wu, Chengmao, Zhang, Xialu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.12.2022
Témata:
Maximum entropy principle
Possibilistic fuzzy clustering
Robust loss function
Validity function
Validity function
Maximum entropy principle
Possibilistic fuzzy clustering
Robust loss function
ISSN:0957-4174, 1873-6793
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.118280