Data Mining, Rough Sets and Granular Computing:

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 diffe...

Full description

Saved in:
Bibliographic Details
Other Authors: Lin, Tsau Young (Editor), Yao, Yiyu Y. (Editor), Zadeh, Lotfi A. (Editor)
Format: Electronic eBook
Language:English
Published: Heidelberg Physica-Verlag HD 2002
Series:Studies in Fuzziness and Soft Computing 95
Subjects:
Online Access:FHI01
BTU01
Volltext
Summary: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
Physical Description:1 Online-Ressource (IX, 537 p)
ISBN:9783790817911
DOI:10.1007/978-3-7908-1791-1

There is no print copy available.

Interlibrary loan Place Request Caution: Not in THWS collection! Get full text