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Temporal Data Mining Unsupervised by Yang Yun (10 results)
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Condition: New. pp. 172.
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Temporal Data Mining Via Unsupervised Ensemble Learning
Language: English
Published by Elsevier Science Ltd, 2016
ISBN 10: 0128116544 ISBN 13: 9780128116548
Paperback. Condition: Brand New. 1st edition. 172 pages. 9.00x7.25x0.75 inches. In Stock.
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£ 37.66
£ 15.49 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPaperback. Condition: New.
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Condition: New. pp. 172.
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Condition: New. pp. 172.
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Temporal Data Mining via Unsupervised Ensemble Learning
Language: English
Published by Elsevier Science Publishing Co Inc, 2016
ISBN 10: 0128116544 ISBN 13: 9780128116548
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
£ 47.65
£ 15.55 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPaperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.
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Condition: new. Questo è un articolo print on demand.
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Temporal Data Mining via Unsupervised Ensemble Learning
Language: English
Published by Elsevier, München, Elsevier, 2016
ISBN 10: 0128116544 ISBN 13: 9780128116548
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 -Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics. Englisch.
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Includes fundamental concepts and knowledge, covering all key tasks and techniques of temporal data mining, i.e., temporal data representations, similarity measure, and mining tasks Concentrates on temporal data clustering tasks from diffe.
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Temporal Data Mining via Unsupervised Ensemble Learning
Language: English
Published by Elsevier, München, Elsevier, 2016
ISBN 10: 0128116544 ISBN 13: 9780128116548
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics.
