Compression Schemes for Mining Large Datasets A Machine Learning Perspective

Language: English

Published by Springer, 2013

1447156064 / 9781447156062

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

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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. Sprache: Englisch Gewicht in Gramm: 442.…

Title
Compression Schemes for Mining Large Datasets A Machine Learning Perspective
Author
S. V. Subrahmanya, M. Narasimha Murty, T. Ravindra Babu
Publisher
Springer
Publication year
2013
Condition
Neu
Binding
Gebundene Ausgabe
Language
English
ISBN 10
1447156064
ISBN 13
9781447156062
Item weight
442 grams
Series
Book 35 of 86: Advances in Computer Vision and Pattern Recognition

Home-set

Mannheim, Germany

5-star seller

AbeBooks seller since April 13, 2026

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