Data Mining, Rough Sets and Granular Computing

Tsau Young Lin (Curatore)

Yiyu Y. Yao (Curatore)

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2002

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.

• Curatore: Tsau Young Lin, Yiyu Y. Yao
• Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
• Collana: Studies in Fuzziness and Soft Computing
• Anno: 2002
• Rilegatura: Hardback
• Pagine: 537 p.
• Dimensioni: 235 x 155 mm
• EAN: 9783790814613

Testo in English