| Data Mining, Rough Sets and Granular Computing Contributor(s): Lin, Tsau Young (Editor), Yao, Yiyu Y. (Editor), Zadeh, Lotfi A. (Editor) |
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ISBN: 3790825085 ISBN-13: 9783790825084 Publisher: Physica-Verlag OUR PRICE: $161.49 Product Type: Paperback - Other Formats Published: October 2010 |
| Additional Information |
| BISAC Categories: - Computers | Intelligence (ai) & Semantics - Medical - Computers | Databases - General |
| Dewey: 006.3 |
| Series: Studies in Fuzziness and Soft Computing |
| Physical Information: 1.11" H x 6.14" W x 9.21" (1.67 lbs) 537 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |