Compression Schemes for Mining Large Datasets

Name: Compression Schemes for Mining Large Datasets
SKU: 26084261
Price: 135.99 AUD
Availability: InStock

A Machine Learning Perspective

By:

Format:

Paperback / softback

Series:

Advances in Computer Vision and Pattern Recognition

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Description

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.

Author Biography:

Dr. T. Ravindra Babu is a Principal Researcher in the E-Commerce Research Labs at Infosys Ltd., Bangalore, India. Mr. S.V. Subrahmanya is Vice President and Research Fellow at the same organization. Dr. M. Narasimha Murty is a Professor in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore, India.

Release date Australia

September 17th, 2016

Author

Series

Advances in Computer Vision and Pattern Recognition

Pages

197

Edition

Softcover reprint of the original 1st ed. 2013

Audience

Professional & Vocational

Illustrations

3 Illustrations, color; 59 Illustrations, black and white; XVI, 197 p. 62 illus., 3 illus. in color.

Dimensions

155x235x12

ISBN-13

9781447170556

Product ID

26084261

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