模糊定量关联规则挖掘

Proceedings 11th International Conference on Tools with Artificial Intelligence Pub Date : 1999-11-08 DOI:10.1109/TAI.1999.809772

Weining Zhang

{"title":"模糊定量关联规则挖掘","authors":"Weining Zhang","doi":"10.1109/TAI.1999.809772","DOIUrl":null,"url":null,"abstract":"Given a relational database and a set of fuzzy terms defined for some attributes we consider the problem of mining fuzzy quantitative association rules that may contain crisp values, intervals, and fuzzy terms in both antecedent and consequent. We present an algorithm extended from the equi-depth partition (EDP) algorithm for solving this problem. Our approach combines interval partition with pre-defined fuzzy terms and is more general.","PeriodicalId":194023,"journal":{"name":"Proceedings 11th International Conference on Tools with Artificial Intelligence","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"87","resultStr":"{\"title\":\"Mining fuzzy quantitative association rules\",\"authors\":\"Weining Zhang\",\"doi\":\"10.1109/TAI.1999.809772\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Given a relational database and a set of fuzzy terms defined for some attributes we consider the problem of mining fuzzy quantitative association rules that may contain crisp values, intervals, and fuzzy terms in both antecedent and consequent. We present an algorithm extended from the equi-depth partition (EDP) algorithm for solving this problem. Our approach combines interval partition with pre-defined fuzzy terms and is more general.\",\"PeriodicalId\":194023,\"journal\":{\"name\":\"Proceedings 11th International Conference on Tools with Artificial Intelligence\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"87\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 11th International Conference on Tools with Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAI.1999.809772\",\"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 11th International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1999.809772","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 87

摘要

给定一个关系数据库和一组为某些属性定义的模糊术语，我们考虑挖掘模糊定量关联规则的问题，这些规则可能在前因式和后因式中包含清晰值、间隔和模糊术语。我们提出了一种从等深度划分(EDP)算法扩展而来的求解该问题的算法。该方法将区间划分与预定义模糊项相结合，具有较好的通用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Mining fuzzy quantitative association rules

Given a relational database and a set of fuzzy terms defined for some attributes we consider the problem of mining fuzzy quantitative association rules that may contain crisp values, intervals, and fuzzy terms in both antecedent and consequent. We present an algorithm extended from the equi-depth partition (EDP) algorithm for solving this problem. Our approach combines interval partition with pre-defined fuzzy terms and is more general.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings 11th International Conference on Tools with Artificial Intelligence

自引率

0.00%

发文量