Lei Jiang, L. Ding, Yunwen Lei, Ming Chen, Zhigao Zeng
{"title":"混合自适应小生境改进粒子群优化聚类算法","authors":"Lei Jiang, L. Ding, Yunwen Lei, Ming Chen, Zhigao Zeng","doi":"10.1109/ICNC.2011.6021907","DOIUrl":null,"url":null,"abstract":"Clustering is an important data analysis and data mining technique. PSO clustering is one of the popular partition algorithm. But it often suffers from the problem of premature convergence and traps in suboptimum solution. This paper uses adaptive niche particle swarm algorithm to improve clustering. And it is also studied the impact of different fitness optimization function to clustering data. The results show that the algorithm which hybrids adaptive niche to PSO clustering techniques has more competitive. It also shows that the new fitness optimization function we proposed is more promising in the fields of high dimension data set and large difference of the number samples in clusters than the popular function of Merwe introduced.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Notice of RetractionHybrid adaptive niche to improve particle swarm optimization clustering algorithm\",\"authors\":\"Lei Jiang, L. Ding, Yunwen Lei, Ming Chen, Zhigao Zeng\",\"doi\":\"10.1109/ICNC.2011.6021907\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clustering is an important data analysis and data mining technique. PSO clustering is one of the popular partition algorithm. But it often suffers from the problem of premature convergence and traps in suboptimum solution. This paper uses adaptive niche particle swarm algorithm to improve clustering. And it is also studied the impact of different fitness optimization function to clustering data. The results show that the algorithm which hybrids adaptive niche to PSO clustering techniques has more competitive. It also shows that the new fitness optimization function we proposed is more promising in the fields of high dimension data set and large difference of the number samples in clusters than the popular function of Merwe introduced.\",\"PeriodicalId\":299503,\"journal\":{\"name\":\"2011 Seventh International Conference on Natural Computation\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Seventh International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2011.6021907\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Seventh International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2011.6021907","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Notice of RetractionHybrid adaptive niche to improve particle swarm optimization clustering algorithm
Clustering is an important data analysis and data mining technique. PSO clustering is one of the popular partition algorithm. But it often suffers from the problem of premature convergence and traps in suboptimum solution. This paper uses adaptive niche particle swarm algorithm to improve clustering. And it is also studied the impact of different fitness optimization function to clustering data. The results show that the algorithm which hybrids adaptive niche to PSO clustering techniques has more competitive. It also shows that the new fitness optimization function we proposed is more promising in the fields of high dimension data set and large difference of the number samples in clusters than the popular function of Merwe introduced.