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paperback. Condition: Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority!
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Large-Scale Parallel Data Mining (Lecture Notes in Computer Science, 1759)
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Add to basketCondition: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
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Large-Scale Parallel Data Mining (Lecture Notes in Computer Science, 1759)
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Large-Scale Parallel Data Mining
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Condition: New. pp. 276.
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Large-Scale Parallel Data Mining
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Withthe unprecedented rate at which data is being collected today in almostall elds of human endeavor, there is an emerging economic and scientic need to extract useful information from it. For example, many companies already have data-warehouses inthe terabyte range (e.g., FedEx, Walmart).The WorldWide Web has an estimated 800 millionweb-pages. Similarly,scienti c data is rea- ing gigantic proportions (e.g., NASA space missions, Human Genome Project). High-performance, scalable, parallel, and distributed computing is crucial for ensuring system scalabilityand interactivityas datasets continue to grow in size and complexity. Toaddress thisneedweorganizedtheworkshoponLarge-ScaleParallelKDD Systems, which was held in conjunction with the 5th ACM SIGKDD Inter- tional Conference on Knowledge Discovery and Data Mining, on August 15th, 1999, San Diego, California. The goal of this workshop was to bring researchers and practitioners together in a setting where they could discuss the design, - plementation,anddeploymentoflarge-scaleparallelknowledgediscovery (PKD) systems, which can manipulate data taken from very large enterprise or sci- tic databases, regardless of whether the data is located centrally or is globally distributed. Relevant topics identie d for the workshop included: { How to develop a rapid-response, scalable, and parallel knowledge discovery system that supports global organizations with terabytes of data.
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Taschenbuch. Condition: Neu. Large-Scale Parallel Data Mining | Mohammed J. Zaki (u. a.) | Taschenbuch | viii | Englisch | 2000 | Springer | EAN 9783540671947 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Large-Scale Parallel Data Mining
Language: English
Published by Springer Berlin Heidelberg Feb 2000, 2000
ISBN 10: 3540671943 ISBN 13: 9783540671947
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Withthe unprecedented rate at which data is being collected today in almostall elds of human endeavor, there is an emerging economic and scientic need to extract useful information from it. For example, many companies already have data-warehouses inthe terabyte range (e.g., FedEx, Walmart).The WorldWide Web has an estimated 800 millionweb-pages. Similarly,scienti c data is rea- ing gigantic proportions (e.g., NASA space missions, Human Genome Project). High-performance, scalable, parallel, and distributed computing is crucial for ensuring system scalabilityand interactivityas datasets continue to grow in size and complexity. Toaddress thisneedweorganizedtheworkshoponLarge-ScaleParallelKDD Systems, which was held in conjunction with the 5th ACM SIGKDD Inter- tional Conference on Knowledge Discovery and Data Mining, on August 15th, 1999, San Diego, California. The goal of this workshop was to bring researchers and practitioners together in a setting where they could discuss the design, - plementation,anddeploymentoflarge-scaleparallelknowledgediscovery (PKD) systems, which can manipulate data taken from very large enterprise or sci- tic databases, regardless of whether the data is located centrally or is globally distributed. Relevant topics identie d for the workshop included: { How to develop a rapid-response, scalable, and parallel knowledge discovery system that supports global organizations with terabytes of data. 272 pp. Englisch.
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Condition: New. Print on Demand pp. 276 Illus.
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Condition: New. PRINT ON DEMAND pp. 276.
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Large-Scale Parallel Data Mining
Language: English
Published by Springer Berlin Heidelberg, 2000
ISBN 10: 3540671943 ISBN 13: 9783540671947
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Large-Scale Parallel Data Mining.- Parallel and Distributed Data Mining: An Introduction.- Mining Frameworks.- The Integrated Delivery of Large-Scale Data Mining: The ACSys Data Mining Project.- A High Performance Implementation of the Data Space Transfer P.
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Large-Scale Parallel Data Mining
Language: English
Published by Springer, Springer Feb 2000, 2000
ISBN 10: 3540671943 ISBN 13: 9783540671947
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Large-Scale Parallel Data Mining.- Parallel and Distributed Data Mining: An Introduction.- Mining Frameworks.- The Integrated Delivery of Large-Scale Data Mining: The ACSys Data Mining Project.- A High Performance Implementation of the Data Space Transfer Protocol (DSTP).- Active Mining in a Distributed Setting.- Associations and Sequences.- Efficient Parallel Algorithms for Mining Associations.- Parallel Branch-and-Bound Graph Search for Correlated Association Rules.- Parallel Generalized Association Rule Mining on Large Scale PC Cluster.- Parallel Sequence Mining on Shared-Memory Machines.- Classification.- Parallel Predictor Generation.- Efficient Parallel Classification Using Dimensional Aggregates.- Learning Rules from Distributed Data.- Clustering.- Collective, Hierarchical Clustering from Distributed, Heterogeneous Data.- A Data-Clustering Algorithm on Distributed Memory Multiprocessors.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 272 pp. Englisch.
