基于粒子群算法的数据聚类

The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI:10.1109/CEC.2003.1299577

D. V. D. Merwe, A. Engelbrecht

{"title":"基于粒子群算法的数据聚类","authors":"D. V. D. Merwe, A. Engelbrecht","doi":"10.1109/CEC.2003.1299577","DOIUrl":null,"url":null,"abstract":"This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"832","resultStr":"{\"title\":\"Data clustering using particle swarm optimization\",\"authors\":\"D. V. D. Merwe, A. Engelbrecht\",\"doi\":\"10.1109/CEC.2003.1299577\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.\",\"PeriodicalId\":416243,\"journal\":{\"name\":\"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.\",\"volume\":\"89 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"832\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2003.1299577\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2003.1299577","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 832

摘要

本文提出了两种利用粒子群算法聚类数据的新方法。演示了如何使用粒子群算法来查找用户指定数量的簇的质心。然后将该算法扩展为使用K-means聚类来为初始群播种。第二种算法基本上是使用粒子群算法来细化由K-means组成的聚类。在6个数据集上对新的粒子群算法进行了评估，并与K-means聚类的性能进行了比较。结果表明，两种粒子群聚类技术都有很大的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Data clustering using particle swarm optimization

This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

The 2003 Congress on Evolutionary Computation, 2003. CEC '03.

自引率

0.00%

发文量