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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.
| Serija: | Studies in Fuzziness and Soft Computing |
| Leidėjas: | Physica-Verlag HD |
| Išleidimo metai: | 2002 |
| Knygos puslapių skaičius: | 548 |
| ISBN-10: | 379081461X |
| ISBN-13: | 9783790814613 |
| Formatas: | 241 x 160 x 34 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Data Mining, Rough Sets and Granular Computing“
