{"title":"一种新的变长染色体表示遗传聚类算法","authors":"Ming-an Zhang, Yong Deng, Dong-xia Chang","doi":"10.1145/2598394.2602272","DOIUrl":null,"url":null,"abstract":"The paper proposed a new genetic clustering algorithm with variable-length chromosome representation (GCVCR), which can automatically evolve and find the optimal number of clusters as well as proper cluster centers of the data set. A new clustering criterion based on message passing between data points and the candidate centers described by the chromosome are presented to make the clustering problem more effective. The simulation results show the effectiveness of the proposed algorithm.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A novel genetic clustering algorithm with variable-length chromosome representation\",\"authors\":\"Ming-an Zhang, Yong Deng, Dong-xia Chang\",\"doi\":\"10.1145/2598394.2602272\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper proposed a new genetic clustering algorithm with variable-length chromosome representation (GCVCR), which can automatically evolve and find the optimal number of clusters as well as proper cluster centers of the data set. A new clustering criterion based on message passing between data points and the candidate centers described by the chromosome are presented to make the clustering problem more effective. The simulation results show the effectiveness of the proposed algorithm.\",\"PeriodicalId\":298232,\"journal\":{\"name\":\"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2598394.2602272\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2598394.2602272","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A novel genetic clustering algorithm with variable-length chromosome representation
The paper proposed a new genetic clustering algorithm with variable-length chromosome representation (GCVCR), which can automatically evolve and find the optimal number of clusters as well as proper cluster centers of the data set. A new clustering criterion based on message passing between data points and the candidate centers described by the chromosome are presented to make the clustering problem more effective. The simulation results show the effectiveness of the proposed algorithm.
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