{"title":"基于模糊形式概念分析的模糊聚类结果解释","authors":"Minyar Sassi Hidri, A. Touzi, Habib Ounelli","doi":"10.1109/FUZZY.2007.4295476","DOIUrl":null,"url":null,"abstract":"The purpose of this paper is to construct structural information from the original data, where the results of fuzzy clustering can be displayed and interpreted. We use fuzzy formal concept analysis (FFCA) based technique for visual data mining and fuzzy clustering results interpretation. The visual interpretation and the navigation in the fuzzy lattice provided useful insights about the overlapping of different clusters and their relationships.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Interpreting Fuzzy Clustering Results based on Fuzzy Formal Concept Analysis\",\"authors\":\"Minyar Sassi Hidri, A. Touzi, Habib Ounelli\",\"doi\":\"10.1109/FUZZY.2007.4295476\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of this paper is to construct structural information from the original data, where the results of fuzzy clustering can be displayed and interpreted. We use fuzzy formal concept analysis (FFCA) based technique for visual data mining and fuzzy clustering results interpretation. The visual interpretation and the navigation in the fuzzy lattice provided useful insights about the overlapping of different clusters and their relationships.\",\"PeriodicalId\":236515,\"journal\":{\"name\":\"2007 IEEE International Fuzzy Systems Conference\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Fuzzy Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.2007.4295476\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Fuzzy Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.2007.4295476","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interpreting Fuzzy Clustering Results based on Fuzzy Formal Concept Analysis
The purpose of this paper is to construct structural information from the original data, where the results of fuzzy clustering can be displayed and interpreted. We use fuzzy formal concept analysis (FFCA) based technique for visual data mining and fuzzy clustering results interpretation. The visual interpretation and the navigation in the fuzzy lattice provided useful insights about the overlapping of different clusters and their relationships.
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