{"title":"基于独立分量分析的k均值聚类谨慎播种","authors":"T. Onoda, Miho Sakai, S. Yamada","doi":"10.1109/WI-IAT.2010.102","DOIUrl":null,"url":null,"abstract":"The k-means method is a widely used clustering technique because of its simplicity and speed. However, the clustering result depends heavily on the chosen initial value. In this report, we propose a seeding method with independent component analysis for the k-means method. Using a benchmark dataset, we evaluate the performance of our proposed method and compare it with other seeding methods.","PeriodicalId":197966,"journal":{"name":"Web Intelligence/IAT Workshops","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Careful Seeding Based on Independent Component Analysis for k-Means Clustering\",\"authors\":\"T. Onoda, Miho Sakai, S. Yamada\",\"doi\":\"10.1109/WI-IAT.2010.102\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The k-means method is a widely used clustering technique because of its simplicity and speed. However, the clustering result depends heavily on the chosen initial value. In this report, we propose a seeding method with independent component analysis for the k-means method. Using a benchmark dataset, we evaluate the performance of our proposed method and compare it with other seeding methods.\",\"PeriodicalId\":197966,\"journal\":{\"name\":\"Web Intelligence/IAT Workshops\",\"volume\":\"2009 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Web Intelligence/IAT Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI-IAT.2010.102\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Web Intelligence/IAT Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI-IAT.2010.102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Careful Seeding Based on Independent Component Analysis for k-Means Clustering
The k-means method is a widely used clustering technique because of its simplicity and speed. However, the clustering result depends heavily on the chosen initial value. In this report, we propose a seeding method with independent component analysis for the k-means method. Using a benchmark dataset, we evaluate the performance of our proposed method and compare it with other seeding methods.
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