{"title":"模糊c均值聚类算法参数优化","authors":"Yongchao Liu, Yunjie Zhang","doi":"10.1109/FSKD.2007.436","DOIUrl":null,"url":null,"abstract":"For overcoming the shortcoming that Fuzzy c-Means (FCM) clustering algorithm seriously depends on the initial values of clustering numbers (c) and fuzzy exponent (m), we introduce genetic algorithm to find the pair parameters of FCM simultaneity. In the proposed algorithm, the clustering numbers and the fuzzy exponent are controlled by a binary code. In order to optimize the two parameters, new methods to code, decode, crossover and establish fitness function have been proposed. Results demonstrating the superiority of the proposed method, as compared to other method that only use validity index to find the clustering numbers (c), are provided for several real-life and artificial data sets.","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Optimizing Parameters of Fuzzy c-Means Clustering Algorithm\",\"authors\":\"Yongchao Liu, Yunjie Zhang\",\"doi\":\"10.1109/FSKD.2007.436\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For overcoming the shortcoming that Fuzzy c-Means (FCM) clustering algorithm seriously depends on the initial values of clustering numbers (c) and fuzzy exponent (m), we introduce genetic algorithm to find the pair parameters of FCM simultaneity. In the proposed algorithm, the clustering numbers and the fuzzy exponent are controlled by a binary code. In order to optimize the two parameters, new methods to code, decode, crossover and establish fitness function have been proposed. Results demonstrating the superiority of the proposed method, as compared to other method that only use validity index to find the clustering numbers (c), are provided for several real-life and artificial data sets.\",\"PeriodicalId\":201883,\"journal\":{\"name\":\"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FSKD.2007.436\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2007.436","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimizing Parameters of Fuzzy c-Means Clustering Algorithm
For overcoming the shortcoming that Fuzzy c-Means (FCM) clustering algorithm seriously depends on the initial values of clustering numbers (c) and fuzzy exponent (m), we introduce genetic algorithm to find the pair parameters of FCM simultaneity. In the proposed algorithm, the clustering numbers and the fuzzy exponent are controlled by a binary code. In order to optimize the two parameters, new methods to code, decode, crossover and establish fitness function have been proposed. Results demonstrating the superiority of the proposed method, as compared to other method that only use validity index to find the clustering numbers (c), are provided for several real-life and artificial data sets.