{"title":"平衡多维聚类的自适应算法","authors":"Clement T. Yu, T. Jiang","doi":"10.1109/ICDE.1988.105482","DOIUrl":null,"url":null,"abstract":"The G-K-D tree (generalized K-D tree) method aims at reducing the average number of data page accesses per query, but it ignores the cost of index search. The authors propose two adaptive algorithms that take into consideration both data page access cost and index page access cost. It attempts to find a minimum total cost. Experimental results indicate that the proposed algorithms are superior to the G-K-D tree method.<<ETX>>","PeriodicalId":243420,"journal":{"name":"Proceedings. Fourth International Conference on Data Engineering","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Adaptive algorithms for balanced multidimensional clustering\",\"authors\":\"Clement T. Yu, T. Jiang\",\"doi\":\"10.1109/ICDE.1988.105482\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The G-K-D tree (generalized K-D tree) method aims at reducing the average number of data page accesses per query, but it ignores the cost of index search. The authors propose two adaptive algorithms that take into consideration both data page access cost and index page access cost. It attempts to find a minimum total cost. Experimental results indicate that the proposed algorithms are superior to the G-K-D tree method.<<ETX>>\",\"PeriodicalId\":243420,\"journal\":{\"name\":\"Proceedings. Fourth International Conference on Data Engineering\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1988-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. Fourth International Conference on Data Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.1988.105482\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. Fourth International Conference on Data Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.1988.105482","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive algorithms for balanced multidimensional clustering
The G-K-D tree (generalized K-D tree) method aims at reducing the average number of data page accesses per query, but it ignores the cost of index search. The authors propose two adaptive algorithms that take into consideration both data page access cost and index page access cost. It attempts to find a minimum total cost. Experimental results indicate that the proposed algorithms are superior to the G-K-D tree method.<>