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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
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Data Mining, Rough Sets and Granular Computing

Data Mining, Rough Sets and Granular Computing

Yao, Yiyu Y.
Zadeh, Lotfi A.
Lin, Tsau Young

Paperback

Series: Studies in Fuzziness and Soft Computing, Book 95

Medical ReferenceDatabasesGeneral Computers

ISBN10: 3790825085
ISBN13: 9783790825084
Publisher: Physica-Verlag
Published: Oct 21 2010
Pages: 537
Weight: 1.67
Height: 1.11 Width: 6.14 Depth: 9.21
Language: English
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.

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