Knowledge Discovery in Databases: PKDD 2005

9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005. Proceedings

  • Alípio Mário Jorge
  • Luís Torgo
  • Pavel Brazdil
  • Rui Camacho
  • João Gama

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3721)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 3721)

Table of contents

  1. Front Matter
  2. Invited Talks

  3. Long Papers

    1. Maurizio Atzori, Francesco Bonchi, Fosca Giannotti, Dino Pedreschi
      Pages 10-21
    2. Björn Bringmann, Albrecht Zimmermann
      Pages 46-58
    3. Lin Dong, Eibe Frank, Stefan Kramer
      Pages 84-95
    4. Pedro Gabriel Ferreira, Paulo J. Azevedo
      Pages 96-107
    5. Chi-Hoon Lee, Russell Greiner, Mark Schmidt
      Pages 121-132
    6. Jurij Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos
      Pages 133-145
    7. Jinyan Li, Haiquan Li, Donny Soh, Limsoon Wong
      Pages 146-156
    8. Ling Li, Amrit Pratap, Hsuan-Tien Lin, Yaser S. Abu-Mostafa
      Pages 157-168

Other volumes

  1. 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005. Proceedings
  2. Knowledge Discovery in Databases: PKDD 2005
    9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005. Proceedings

About these proceedings

Introduction

The European Conference on Machine Learning (ECML) and the European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) were jointly organized this year for the ?fth time in a row, after some years of mutual independence before. After Freiburg (2001), Helsinki (2002), Cavtat (2003) and Pisa (2004), Porto received the 16th edition of ECML and the 9th PKDD in October 3–7. Having the two conferences together seems to be working well: 585 di?erent paper submissions were received for both events, which maintains the high s- mission standard of last year. Of these, 335 were submitted to ECML only, 220 to PKDD only and 30 to both. Such a high volume of scienti?c work required a tremendous e?ort from Area Chairs, Program Committee members and some additional reviewers. On average, PC members had 10 papers to evaluate, and Area Chairs had 25 papers to decide upon. We managed to have 3 highly qua- ?edindependentreviewsperpaper(withveryfewexceptions)andoneadditional overall input from one of the Area Chairs. After the authors’ responses and the online discussions for many of the papers, we arrived at the ?nal selection of 40 regular papers for ECML and 35 for PKDD. Besides these, 32 others were accepted as short papers for ECML and 35 for PKDD. This represents a joint acceptance rate of around 13% for regular papers and 25% overall. We thank all involved for all the e?ort with reviewing and selection of papers. Besidesthecoretechnicalprogram,ECMLandPKDDhad6invitedspeakers, 10 workshops, 8 tutorials and a Knowledge Discovery Challenge.

Keywords

SVM classification algorithmic learning association rule mining bayesian learning clustering data analysis data mining database decision trees kernel methods knowledge discovery learning classifier systems multi-relational data mining pattern mining

Editors and affiliations

  • Alípio Mário Jorge
    • 1
  • Luís Torgo
    • 2
  • Pavel Brazdil
    • 3
  • Rui Camacho
    • 4
  • João Gama
    • 5
  1. 1.LIACC/FEPUniversidade do PortoPortugal
  2. 2.LIAAD-INESC Porto LA / FEPUniversity of PortoPortoPortugal
  3. 3.LIAAD-INESC Porto L.A./Faculty of EconomicsUniversity of PortoPortoPortugal
  4. 4.Faculdade de Engenharia & LIAADUniversidade do PortoPortugal
  5. 5.Faculty of Economics of the University of PortoPortugal

Bibliographic information

  • DOI https://doi.org/10.1007/11564126
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-29244-9
  • Online ISBN 978-3-540-31665-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book