The Springer International Series in Engineering and Computer Science

Knowledge Discovery and Measures of Interest

Authors: Hilderman, Robert, Hamilton, Howard J.

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  • ISBN 978-1-4757-3283-2
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About this book

Knowledge Discovery and Measures of Interest is a reference book for knowledge discovery researchers, practitioners, and students. The knowledge discovery researcher will find that the material provides a theoretical foundation for measures of interest in data mining applications where diversity measures are used to rank summaries generated from databases. The knowledge discovery practitioner will find solid empirical evidence on which to base decisions regarding the choice of measures in data mining applications. The knowledge discovery student in a senior undergraduate or graduate course in databases and data mining will find the book is a good introduction to the concepts and techniques of measures of interest.
In Knowledge Discovery and Measures of Interest, we study two closely related steps in any knowledge discovery system: the generation of discovered knowledge; and the interpretation and evaluation of discovered knowledge. In the generation step, we study data summarization, where a single dataset can be generalized in many different ways and to many different levels of granularity according to domain generalization graphs. In the interpretation and evaluation step, we study diversity measures as heuristics for ranking the interestingness of the summaries generated.
The objective of this work is to introduce and evaluate a technique for ranking the interestingness of discovered patterns in data. It consists of four primary goals:

  • To introduce domain generalization graphs for describing and guiding the generation of summaries from databases.
  • To introduce and evaluate serial and parallel algorithms that traverse the domain generalization space described by the domain generalization graphs.
  • To introduce and evaluate diversity measures as heuristic measures of interestingness for ranking summaries generated from databases.
  • To develop the preliminary foundation for a theory of interestingness within the context of ranking summaries generated from databases.
Knowledge Discovery and Measures of Interest is suitable as a secondary text in a graduate level course and as a reference for researchers and practitioners in industry.

Table of contents (7 chapters)

  • Introduction

    Hilderman, Robert J. (et al.)

    Pages 1-9

  • Background and Related Work

    Hilderman, Robert J. (et al.)

    Pages 11-23

  • A Data Mining Technique

    Hilderman, Robert J. (et al.)

    Pages 25-35

  • Heuristic Measures of Interestingness

    Hilderman, Robert J. (et al.)

    Pages 37-46

  • An Interestingness Framework

    Hilderman, Robert J. (et al.)

    Pages 47-97

Buy this book

eBook $69.99
price for USA (gross)
  • ISBN 978-1-4757-3283-2
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $229.00
price for USA
  • ISBN 978-0-7923-7507-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $89.95
price for USA
  • ISBN 978-1-4419-4913-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Knowledge Discovery and Measures of Interest
Authors
Series Title
The Springer International Series in Engineering and Computer Science
Series Volume
638
Copyright
2001
Publisher
Springer US
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4757-3283-2
DOI
10.1007/978-1-4757-3283-2
Hardcover ISBN
978-0-7923-7507-4
Softcover ISBN
978-1-4419-4913-4
Series ISSN
0893-3405
Edition Number
1
Number of Pages
XVIII, 162
Topics