Ratio component-wise design method of fuzzy c-means clustering validity function

作     者：Wang, Guan Wang, Jie-Sheng Wang, Hong-Yu Liu, Jia-Xu 

作者机构：Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Peoples R China 

出 版 物：《JOURNAL OF INTELLIGENT & FUZZY SYSTEMS》 (智能与模糊系统杂志)

年 卷 期：2022年第43卷第4期

页      面：4691-4707页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Basic Scientific Research Project of Institution of Higher Learning of Liaoning Province [LJKZ0293] Project by Liaoning Provincial Natural Science Foundation of China 

主　　题：Data mining fuzzy c-means clustering algorithm clustering validity function ratio component-wise design 

摘      要：Fuzzy clustering is an important research field in pattern recognition, machine learning and image processing. The fuzzy C-means (FCM) clustering algorithm is one of the most common fuzzy clustering algorithms. However, it requires a given number of clusters in advance for accurate clustering of data sets, so it is necessary to put forward a better clustering validity index to verify the clustering results. This paper presents a ratio component-wise design method of clustering validity function based on FCM clustering method. By permutation and combination of six clustering validity components representing different meanings in the form of ratio, 49 different clustering validity functions are formed. Then, these functions are verified experimentally under six kinds of UCI data sets, and a clustering validity function with the simplest structure and the best classification effect is selected by comparison. Finally, this function is compared with seven traditional clustering validity functions on eight UCI data sets. The simulation results show that the proposed validity function can better verify the classification results and determine the optimal clustering number of different data sets.