{"title":"通过点邻域聚类兼容对象","authors":"Renxia Wan, Lixin Wang, Zijun Hao","doi":"10.1109/ICAIE.2010.5641428","DOIUrl":null,"url":null,"abstract":"In some cases, clustering objects into several compatible clusters is more rational than traditional clustering methods do. In this paper, we propose a new compatible clustering algorithm based on CompClustering[8], it adopts point neighborhood technique to replace the iterative mechanism of the latter. Experiments show that the proposed algorithm can get some consistent clustering results, and theory analysis also demonstrates that the proposed algorithm has lower computation consumption than CompClustering does.","PeriodicalId":216006,"journal":{"name":"2010 International Conference on Artificial Intelligence and Education (ICAIE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Clustering compatible objects by point neighborhood\",\"authors\":\"Renxia Wan, Lixin Wang, Zijun Hao\",\"doi\":\"10.1109/ICAIE.2010.5641428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In some cases, clustering objects into several compatible clusters is more rational than traditional clustering methods do. In this paper, we propose a new compatible clustering algorithm based on CompClustering[8], it adopts point neighborhood technique to replace the iterative mechanism of the latter. Experiments show that the proposed algorithm can get some consistent clustering results, and theory analysis also demonstrates that the proposed algorithm has lower computation consumption than CompClustering does.\",\"PeriodicalId\":216006,\"journal\":{\"name\":\"2010 International Conference on Artificial Intelligence and Education (ICAIE)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Artificial Intelligence and Education (ICAIE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAIE.2010.5641428\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Artificial Intelligence and Education (ICAIE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIE.2010.5641428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Clustering compatible objects by point neighborhood
In some cases, clustering objects into several compatible clusters is more rational than traditional clustering methods do. In this paper, we propose a new compatible clustering algorithm based on CompClustering[8], it adopts point neighborhood technique to replace the iterative mechanism of the latter. Experiments show that the proposed algorithm can get some consistent clustering results, and theory analysis also demonstrates that the proposed algorithm has lower computation consumption than CompClustering does.