Data Mining, Rough Sets and Granular Computing

丛 书 名：Studies in Fuzziness and Soft Computing

版本说明：1

作     者：Tsau Young Lin Yiyu Y. Yao Lotfi A. Zadeh 

I S B N：(纸本) 9783790814613;9783790825084 

出 版 社：Physica Heidelberg 

出 版 年：2002年

页      数：IX, 537页

主 题 词：Artificial Intelligence Database Management 

学科分类：0810[工学-信息与通信工程] 07[理学] 08[工学] 080401[工学-精密仪器及机械] 070104[理学-应用数学] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 081002[工学-信号与信息处理] 0701[理学-数学] 

摘      要：During the past few years, data mining has grown rapidly in visibility and importance within information processing and decision analysis. This is par­ ticularly true in the realm of e-commerce, where data mining is moving from a nice-to-have to a must-have status. In a different though related context, a new computing methodology called granular computing is emerging as a powerful tool for the conception, analysis and design of information/intelligent systems. In essence, data mining deals with summarization of information which is resident in large data sets, while granular computing plays a key role in the summarization process by draw­ ing together points (objects) which are related through similarity, proximity or functionality. In this perspective, granular computing has a position of centrality in data mining. Another methodology which has high relevance to data mining and plays a central role in this volume is that of rough set theory. Basically, rough set theory may be viewed as a branch of granular computing. However, its applications to data mining have predated that of granular computing.