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Data Mining, Rough Sets and Granular Computing

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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.


Request Code : ZLIBIO1143836
Categories:
Year:
2002
Edition:
1
Publisher:
Physica-Verlag Heidelberg
Language:
English
Pages:
537
ISBN:
978-3-7908-2508-4,978-3-7908-1791-1
Series:
Studies in Fuzziness and Soft Computing 95

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