Data Mining, Rough Sets and Granular Computing: 95 (Studies in Fuzziness and Soft Computing) - Tapa blanda
Sinopsis
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.
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Reseña del editor
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.
Reseña del editor
This volume is the result of a two-year project aimed at coalescing the concepts and techniques of granular computing on one side, and rough set theory on another. It consists of a collection of up-to-date and authoritative expositions of the basic theories underlying data mining, granular computing and rough set theory, and stresses their wide-ranging applications. A principal aim of the work is to stimulate an exploration of ways in which progress in data mining can be enhanced through integration with granular computing and rough set theory.
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Data Mining, Rough Sets and Granular Computing (eng)
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Data Mining, Rough Sets and Granular Computing (Studies in Fuzziness and Soft Computing)
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Data Mining, Rough Sets and Granular Computing
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 548 pp. Englisch. Nº de ref. del artículo: 9783790825084
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Taschenbuch. Condición: Neu. Data Mining, Rough Sets and Granular Computing | Tsau Young Lin (u. a.) | Taschenbuch | ix | Englisch | 2010 | Physica | EAN 9783790825084 | Verantwortliche Person für die EU: Physica Verlag in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 107023078
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Data Mining, Rough Sets and Granular Computing
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Physica-Verlag HD Okt 2010, 2010
ISBN 10: 3790825085
ISBN 13: 9783790825084
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Physica Verlag, Tiergartenstr. 17, 69121 Heidelberg 548 pp. Englisch. Nº de ref. del artículo: 9783790825084
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783790825084
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