Quality Measures in Data Mining

  • Fabrice J. Guillet
  • Howard J. Hamilton

Part of the Studies in Computational Intelligence book series (SCI, volume 43)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Overviews on rule quality

    1. Front Matter
      Pages 1-1
    2. Liqiang Geng, Howard J. Hamilton
      Pages 3-24
    3. Xuan-Hiep Huynh, Fabrice Guillet, Julien Blanchard, Pascale Kuntz, Henri Briand, Régis Gras
      Pages 25-50
    4. Philippe Lenca, Benoît Vaillant, Patrick Meyer, Stephane Lallich
      Pages 51-76
    5. Béatrice Duval, Ansaf Salleb, Christel Vrain
      Pages 77-98
  3. From data to rule quality

  4. Rule quality and validation

    1. Front Matter
      Pages 206-206
    2. Jean Diatta, Henri Ralambondrainy, André Totohasina
      Pages 237-250
    3. Stephane Lallich, Olivier Teytaud, Elie Prudhomme
      Pages 251-275
  5. Back Matter
    Pages 303-313

About this book

Introduction

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.

Keywords

Quality Measures Statistica classification clustering complexity computational intelligence computer-aided design (CAD) data mining intelligence knowledge modeling simulation

Editors and affiliations

  • Fabrice J. Guillet
    • 1
  • Howard J. Hamilton
    • 2
  1. 1.LINA-CNRS FRE 2729Ecole polytechnique de l'université de NantesFrance
  2. 2.Department of Computer ScienceUniversity of ReginaCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-44918-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-44911-9
  • Online ISBN 978-3-540-44918-8
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
Industry Sectors
Automotive
Chemical Manufacturing
Biotechnology
Electronics
Telecommunications
Consumer Packaged Goods
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences