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© 2013

Compression Schemes for Mining Large Datasets

A Machine Learning Perspective

Book

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

Table of contents

  1. Front Matter
    Pages I-XVI
  2. T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 1-10
  3. T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 11-46
  4. T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 47-66
  5. T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 67-94
  6. T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 95-124
  7. T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 125-145
  8. T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 147-172
  9. T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
    Pages 173-183
  10. Back Matter
    Pages 185-197

About this book

Introduction

As data mining algorithms are typically applied to sizable volumes of high-dimensional data, these can result in large storage requirements and inefficient computation times.

This unique text/reference 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, as illustrated using high-dimensional handwritten digit data and a large intrusion detection dataset.

Topics and features: 

  • Presents a concise introduction to data mining paradigms, data compression, and mining compressed data
  • 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 

A must-read for all researchers involved in data mining and big data, the book proposes each algorithm within a discussion of the wider context, implementation details and experimental results. These are further supported by bibliographic notes and a glossary.

Keywords

Classification Clustering Data Abstraction Generation Data Compression High-Dimensional Datasets

Authors and affiliations

  1. 1.Infosys Technologies Ltd.BangaloreIndia
  2. 2.Indian Institute of ScienceBangaloreIndia
  3. 3.Infosys Technologies Ltd.BangaloreIndia

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
  • Series Abbreviated Title Advs Comp. Vision, Pattern Recognition
  • DOI https://doi.org/10.1007/978-1-4471-5607-9
  • Copyright Information Springer-Verlag London 2013
  • Publisher Name Springer, London
  • eBook Packages Computer Science Computer Science (R0)
  • Hardcover ISBN 978-1-4471-5606-2
  • Softcover ISBN 978-1-4471-7055-6
  • eBook ISBN 978-1-4471-5607-9
  • 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
  • Buy this book on publisher's site
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