{"title":"基于模糊ISODATA聚类的土地选择KPI因素分析","authors":"Chengjie Li, Zhen Liu","doi":"10.1109/KAM.2010.5646311","DOIUrl":null,"url":null,"abstract":"Clustering is an example of a class of optimization problems. In the classical clustering, an item must belong to any one cluster. But fuzzy clustering describes more accurately the ambiguous type of structure in data. The fuzzy ISODATA clustering exhibits the rapid convergence in finding the best classification program when the classification number is given. In this paper, we propose the algorithm to solve the choosing lands problem and show the result of the experiment. The result is satisfied to realtors in choosing lands.","PeriodicalId":160788,"journal":{"name":"2010 Third International Symposium on Knowledge Acquisition and Modeling","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis on KPI factors to choose lands with fuzzy ISODATA clustering\",\"authors\":\"Chengjie Li, Zhen Liu\",\"doi\":\"10.1109/KAM.2010.5646311\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clustering is an example of a class of optimization problems. In the classical clustering, an item must belong to any one cluster. But fuzzy clustering describes more accurately the ambiguous type of structure in data. The fuzzy ISODATA clustering exhibits the rapid convergence in finding the best classification program when the classification number is given. In this paper, we propose the algorithm to solve the choosing lands problem and show the result of the experiment. The result is satisfied to realtors in choosing lands.\",\"PeriodicalId\":160788,\"journal\":{\"name\":\"2010 Third International Symposium on Knowledge Acquisition and Modeling\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Third International Symposium on Knowledge Acquisition and Modeling\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KAM.2010.5646311\",\"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 Third International Symposium on Knowledge Acquisition and Modeling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KAM.2010.5646311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis on KPI factors to choose lands with fuzzy ISODATA clustering
Clustering is an example of a class of optimization problems. In the classical clustering, an item must belong to any one cluster. But fuzzy clustering describes more accurately the ambiguous type of structure in data. The fuzzy ISODATA clustering exhibits the rapid convergence in finding the best classification program when the classification number is given. In this paper, we propose the algorithm to solve the choosing lands problem and show the result of the experiment. The result is satisfied to realtors in choosing lands.
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