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  • Book
  • © 2009

Non-Standard Parameter Adaptation for Exploratory Data Analysis

  • Presents novel methods of parameter adaptation in machine learning

  • Valuable contribution to create a true artificial intelligence

  • Recent research in Reinforcement learning, cross entropy and artificial immune systems for exploratory data analysis

Part of the book series: Studies in Computational Intelligence (SCI, volume 249)

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eBook USD 159.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-04005-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
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  • Tax calculation will be finalised during checkout
Softcover Book USD 209.00
Price excludes VAT (USA)
Hardcover Book USD 179.99
Price excludes VAT (USA)

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Table of contents (11 chapters)

  1. Front Matter

  2. Introduction

    • Wesam Ashour Barbakh, Ying Wu, Colin Fyfe
    Pages 1-6
  3. Review of Clustering Algorithms

    • Wesam Ashour Barbakh, Ying Wu, Colin Fyfe
    Pages 7-28
  4. Review of Linear Projection Methods

    • Wesam Ashour Barbakh, Ying Wu, Colin Fyfe
    Pages 29-48
  5. Non-standard Clustering Criteria

    • Wesam Ashour Barbakh, Ying Wu, Colin Fyfe
    Pages 49-72
  6. Topographic Mappings and Kernel Clustering

    • Wesam Ashour Barbakh, Ying Wu, Colin Fyfe
    Pages 73-84
  7. Online Clustering Algorithms and Reinforcement Learning

    • Wesam Ashour Barbakh, Ying Wu, Colin Fyfe
    Pages 85-108
  8. Connectivity Graphs and Clustering with Similarity Functions

    • Wesam Ashour Barbakh, Ying Wu, Colin Fyfe
    Pages 109-122
  9. Reinforcement Learning of Projections

    • Wesam Ashour Barbakh, Ying Wu, Colin Fyfe
    Pages 123-149
  10. Cross Entropy Methods

    • Wesam Ashour Barbakh, Ying Wu, Colin Fyfe
    Pages 151-174
  11. Artificial Immune Systems

    • Wesam Ashour Barbakh, Ying Wu, Colin Fyfe
    Pages 175-197
  12. Conclusions

    • Wesam Ashour Barbakh, Ying Wu, Colin Fyfe
    Pages 199-205
  13. Back Matter

About this book

Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets.

We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods.

We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation.

Keywords

  • Clustering
  • data analysis
  • data mining
  • knowledge discovery
  • machine learning
  • principal component analysis
  • reinforcement learning

Authors and Affiliations

  • Computer Engineering Department, Faculty of Engineering, Islamic University of Gaza, Gaza, Palestine

    Wesam Ashour Barbakh

  • Coastal and Marine Resources Centre, University College Cork, Irish Naval Base, Haulbowline, Cobh, Co. Cork, Ireland

    Ying Wu

  • School of Computing, Applied Computational Intelligence, Research Unit, University of the West of Scotland, Paisley, Scotland, UK

    Colin Fyfe

Bibliographic Information

Buying options

eBook USD 159.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-04005-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 209.00
Price excludes VAT (USA)
Hardcover Book USD 179.99
Price excludes VAT (USA)