Compression Schemes for Mining Large Datasets

Language: English

Published by Springer London Sep 2016, 2016

1447170555 / 9781447170556

Series: Book 35 of 86 - Advances in Computer Vision and Pattern Recognition

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This item is printed on demand - it takes 3-4 days longer - Neuware -This book addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy. Features: describes a non-lossy compression scheme based on run-length encoding of patterns with binary valued features; proposes a lossy compression scheme that recognizes a pattern as a sequence of features and identifying subsequences; examines whether the identification of prototypes and features can be achieved simultaneously through lossy compression and efficient clustering; discusses ways to make use of domain knowledge in generating abstraction; reviews optimal prototype selection using genetic algorithms; suggests possible ways of dealing with big data problems using multiagent systems. 216 pp. Englisch. …

Title
Compression Schemes for Mining Large Datasets
Author
T. Ravindra Babu
Publisher
Springer London Sep 2016
Publication year
2016
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
1447170555
ISBN 13
9781447170556
Item weight
335 grams
Dimensions
235x155x12 mm
Series
Book 35 of 86: Advances in Computer Vision and Pattern Recognition

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

Shipping rates from Germany to U.S.A.

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