{"title":"基于加权核k均值的非线性数据聚类新方法","authors":"A. Rasouli, M. A. Maarof, M. Shamsi","doi":"10.1109/SoCPaR.2009.17","DOIUrl":null,"url":null,"abstract":"Clustering is the process of gathering objects into groups based on their feature’s similarity. In this paper, we concentrate on Weighted Kernel K-Means method for its capability to manage nonlinear separability and high dimensionality in the data. A new slight modification of WKM algorithm has been proposed and tested on real Rice data. The results show that the accuracy of proposed algorithm is higher than other famous clustering algorithm and ensures that the WKM is a good solution for real world problems.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A New Clustering Method Based on Weighted Kernel K-Means for Non-linear Data\",\"authors\":\"A. Rasouli, M. A. Maarof, M. Shamsi\",\"doi\":\"10.1109/SoCPaR.2009.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clustering is the process of gathering objects into groups based on their feature’s similarity. In this paper, we concentrate on Weighted Kernel K-Means method for its capability to manage nonlinear separability and high dimensionality in the data. A new slight modification of WKM algorithm has been proposed and tested on real Rice data. The results show that the accuracy of proposed algorithm is higher than other famous clustering algorithm and ensures that the WKM is a good solution for real world problems.\",\"PeriodicalId\":284743,\"journal\":{\"name\":\"2009 International Conference of Soft Computing and Pattern Recognition\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference of Soft Computing and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SoCPaR.2009.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference of Soft Computing and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SoCPaR.2009.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Clustering Method Based on Weighted Kernel K-Means for Non-linear Data
Clustering is the process of gathering objects into groups based on their feature’s similarity. In this paper, we concentrate on Weighted Kernel K-Means method for its capability to manage nonlinear separability and high dimensionality in the data. A new slight modification of WKM algorithm has been proposed and tested on real Rice data. The results show that the accuracy of proposed algorithm is higher than other famous clustering algorithm and ensures that the WKM is a good solution for real world problems.
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