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Compression Schemes for Mining Large Datasets

Compression Schemes for Mining Large Datasets

A Machine Learning Perspective

by M. Narasimha MurtyT. Ravindra Babu and S.V. Subrahmanya
Hardback
Publication Date: 14/11/2013
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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.
ISBN:
9781447156062
9781447156062
Category:
Data mining
Format:
Hardback
Publication Date:
14-11-2013
Language:
English
Publisher:
Springer London Ltd
Country of origin:
United Kingdom
Pages:
197
Dimensions (mm):
235x155x14mm
Weight:
0.5kg

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