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Compression Schemes for Mining Large Datasets

A Machine Learning Perspective

  • Book
  • © 2013

Overview

Authors:
  1. T. Ravindra Babu

    1. Infosys Technologies Ltd., Bangalore, India

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  2. M. Narasimha Murty

    1. Indian Institute of Science, Bangalore, India

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  3. S.V. Subrahmanya

    1. Infosys Technologies Ltd., Bangalore, India

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  • Examines all aspects of data abstraction generation using a least number of database scans
  • Discusses compressing data through novel lossy and non-lossy schemes
  • Proposes schemes for carrying out clustering and classification directly in the compressed domain
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)

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Table of contents (8 chapters)

  1. Front Matter

    Pages I-XVI
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  2. Introduction

    • T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 1-10

  3. Data Mining Paradigms

    • T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 11-46

  4. Run-Length-Encoded Compression Scheme

    • T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 47-66

  5. Dimensionality Reduction by Subsequence Pruning

    • T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 67-94

  6. Data Compaction Through Simultaneous Selection of Prototypes and Features

    • T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 95-124

  7. Domain Knowledge-Based Compaction

    • T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 125-145

  8. Optimal Dimensionality Reduction

    • T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 147-172

  9. Big Data Abstraction Through Multiagent Systems

    • T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 173-183

  10. Back Matter

    Pages 185-197
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Keywords

About this book

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.

Authors and Affiliations

  • Infosys Technologies Ltd., Bangalore, India

    T. Ravindra Babu, S.V. Subrahmanya

  • Indian Institute of Science, Bangalore, India

    M. Narasimha Murty

About the authors

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.

Bibliographic Information

  • Book Title: Compression Schemes for Mining Large Datasets

  • Book Subtitle: A Machine Learning Perspective

  • Authors: T. Ravindra Babu, M. Narasimha Murty, S.V. Subrahmanya

  • Series Title: Advances in Computer Vision and Pattern Recognition

  • DOI: https://doi.org/10.1007/978-1-4471-5607-9

  • Publisher: Springer London

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag London 2013

  • Hardcover ISBN: 978-1-4471-5606-2Published: 04 December 2013

  • Softcover ISBN: 978-1-4471-7055-6Published: 17 September 2016

  • eBook ISBN: 978-1-4471-5607-9Published: 19 November 2013

  • Series ISSN: 2191-6586

  • Series E-ISSN: 2191-6594

  • Edition Number: 1

  • Number of Pages: XVI, 197

  • Number of Illustrations: 59 b/w illustrations, 3 illustrations in colour

  • Topics: Pattern Recognition, Data Mining and Knowledge Discovery, Artificial Intelligence

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