Overview
- Recent advances in quality measures in data mining
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 43)
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Table of contents (12 chapters)
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Front Matter
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Overviews on rule quality
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Front Matter
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From data to rule quality
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Front Matter
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Rule quality and validation
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Front Matter
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Back Matter
Keywords
About this book
Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst. The user is confronted with the task of selecting the pieces of knowledge that are of the highest quality or interest according to his or her preferences. Since this selection is sometimes a daunting task, designing quality and interestingness measures has become an important challenge for data mining researchers in the last decade.
This volume presents the state of the art concerning quality and interestingness measures for data mining. The book summarizes recent developments and presents original research on this topic. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. Both theoretical and applied chapters are included. Papers for this book were selected and reviewed for correctness and completeness by an international review committee.
Editors and Affiliations
Bibliographic Information
Book Title: Quality Measures in Data Mining
Editors: Fabrice J. Guillet, Howard J. Hamilton
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-540-44918-8
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2007
Hardcover ISBN: 978-3-540-44911-9Published: 08 January 2007
Softcover ISBN: 978-3-642-07952-8Published: 18 November 2010
eBook ISBN: 978-3-540-44918-8Published: 17 January 2007
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIV, 314
Topics: Mathematical and Computational Engineering, Artificial Intelligence
Industry Sectors: Aerospace, Automotive, Biotechnology, Electronics, Energy, Utilities & Environment, Engineering, IT & Software, Oil, Gas & Geosciences, Telecommunications