A dynamic fuzzy clustering method based on genetic algorithm

作     者：ZHENG Yan 1*, ZHOU Chunguang 2, LIANG Yanchun 2 and GUO Dongwei 2(1. College of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. College of Computer Science and Technology, Jilin University, Changchun 130012, China) 

作者机构：College of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China College of Computer Science and Technology Jilin University Changchun 130012 China 

出 版 物：《Progress in Natural Science:Materials International》 (自然科学进展·国际材料(英文))

年 卷 期：2003年第12期

页      面：52-55页

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)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：SupportedbytheNationalNaturalScienceFoundationofChina (GrantNo .60 175 0 2 4) theKeyProjectofChineseMinistryofEducation (No .0 2 0 90 )andtheKeyLaboratoryforSymbolComputationandKnowledgeEngineeringofChineseMinistryofEducation 

主　　题：dynamic fuzzy clustering fuzzy dissimilarity matrix genetic algorithm fuzzy c means clustering. 

摘      要：A dynamic fuzzy clustering method is presented based on the genetic algorithm. By calculating the fuzzy dissimilarity between samples the essential associations among samples are modeled factually. The fuzzy dissimilarity between two samples is mapped into their Euclidean distance, that is, the high dimensional samples are mapped into the two dimensional plane. The mapping is optimized globally by the genetic algorithm, which adjusts the coordinates of each sample, and thus the Euclidean distance, to approximate to the fuzzy dissimilarity between samples gradually. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples, which improves the flexibility and visualization. This method possesses characteristics of a faster convergence rate and more exact clustering than some typical clustering algorithms. Simulated experiments show the feasibility and availability of the proposed method.