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Book synopsis

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.

Book details

• Full title: Data Mining, Rough Sets and Granular Computing
• Author: Tsau Young Lin, Yiyu Y. Yao, Lotfi A. Zadeh
• Language: English
• Binding: Paperback
• Number of pages: 548
• EAN: 9783662003213
• ID: 43423023
• Publisher: Springer Nature B.V.
• Weight: 760 g
• Dimensions: 234 × 156 mm
• Date of publishing: 12. March 2014

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