A Weight Possibilistic Fuzzy C-Means Clustering Algorithm

• Published in: Scientific programming Vol. 2021; pp. 1 - 10
• Main Authors: Chen, Jiashun, Zhang, Hao, Pi, Dechang, Kantardzic, Mehmed, Yin, Qi, Liu, Xin
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Clustering; Data analysis; Data mining; Datasets; Euler-Lagrange equation; Fuzzy sets; Iterative methods; Noise; Parameter modification; Pattern recognition; Process parameters; Weight
• ISSN: 1058-9244; 1875-919X
• DOI: 10.1155/2021/9965813

Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. Especially, parameters in FCM have influence on clustering results. However, a lot of FCM algorithm did not solve the problem, that is, how to set parameters. In this study, we present a kind of method for computing parameters values according to role of parameters in the clustering process. New parameters are assigned to membership and typicality so as to modify objective function, on the basis of which Lagrange equation is constructed and iterative equation of membership is acquired, so does the typicality and center equation. At last, a new possibilistic fuzzy C-means based on the weight parameter algorithm (WPFCM) was proposed. In order to test the efficiency of the algorithm, some experiments on different datasets are conducted to compare WPFCM with FCM, possibilistic C-means (PCM), and possibilistic fuzzy C-means (PFCM). Experimental results show that iterative times of WPFCM are less than FCM about 25% and PFCM about 65% on dataset X12. Resubstitution errors of WPFCM are less than FCM about 19% and PCM about 74% and PFCM about 10% on the IRIS dataset.