knowledge discovery multiple databases von zhang shichao (32 Ergebnisse)

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.

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Gebunden. Zustand: New. The only book that covers the emerging topic of multiple database mining Focuses on developing new techniques for multi-database miningUrgent need to analyse data in multi-databases as a great deal of multi-databases are widely used in orga.

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Zustand: New. 2012. Softcover reprint of the original 1st ed. 2004. Paperback. Series: Advanced Information and Knowledge Processing. Num Pages: 245 pages, 40 black & white tables, biography. BIC Classification: UB; UNH. Category: (P) Professional & Vocational. Dimension: 236 x 157 x 13. Weight in Grams: 366. . . . . .

Zustand: New. A state of the art research monograph that develops the specific and increasingly important area of multiple database mining. Series: Advanced Information and Knowledge Processing. Num Pages: 245 pages, 40 black & white tables, biography. BIC Classification: UN. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 15. Weight in Grams: 1170. . 2004. Hardback. . . . .

Buch. Zustand: Neu. Neuware - Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.

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Paperback. Zustand: new. Paperback. Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Zustand: New. 2012. Softcover reprint of the original 1st ed. 2004. Paperback. Series: Advanced Information and Knowledge Processing. Num Pages: 245 pages, 40 black & white tables, biography. BIC Classification: UB; UNH. Category: (P) Professional & Vocational. Dimension: 236 x 157 x 13. Weight in Grams: 366. . . . . . Books ship from the US and Ireland.

Hardcover. Zustand: new. Hardcover. The Web has emerged as a large, distributed data repository, & information on the Internet & in existing transaction databases can be analyzed for commercial gains in decision making. Therefore, how to efficiently identify quality knowledge from different data sources uncovers a significant challenge. Knowledge Discovery in Multiple Databases provides a comprehensive introduction to the latest advancements in multi-database mining, & presents a local-pattern analysis framework for pattern discovery from multiple data sources. Based on this framework, data preparation techniques in multiple databases, an application-independent database classification for data reduction, & efficient algorithms for pattern discovery from multiple databases are described. This book is suitable for researchers, professionals & students in data mining, distributed data analysis, and machine learning. It is also appropriate for use as a text supplement for broader courses involving knowledge discovery in databases & data mining. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Zustand: New. A state of the art research monograph that develops the specific and increasingly important area of multiple database mining. Series: Advanced Information and Knowledge Processing. Num Pages: 245 pages, 40 black & white tables, biography. BIC Classification: UN. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 15. Weight in Grams: 1170. . 2004. Hardback. . . . . Books ship from the US and Ireland.

Paperback. Zustand: new. Paperback. Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Zustand: new. Hardcover. The Web has emerged as a large, distributed data repository, & information on the Internet & in existing transaction databases can be analyzed for commercial gains in decision making. Therefore, how to efficiently identify quality knowledge from different data sources uncovers a significant challenge. Knowledge Discovery in Multiple Databases provides a comprehensive introduction to the latest advancements in multi-database mining, & presents a local-pattern analysis framework for pattern discovery from multiple data sources. Based on this framework, data preparation techniques in multiple databases, an application-independent database classification for data reduction, & efficient algorithms for pattern discovery from multiple databases are described. This book is suitable for researchers, professionals & students in data mining, distributed data analysis, and machine learning. It is also appropriate for use as a text supplement for broader courses involving knowledge discovery in databases & data mining. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The only book that covers the emerging topic of multiple database mining Focuses on developing new techniques for multi-database miningUrgent need to analyse data in multi-databases as a great deal of multi-databases are widely used in orga.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 248 pp. Englisch.