Knowledge Discovery in Databases: PKDD 2005

1. Front Matter

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2. Invited Talks

   1. Data Analysis in the Life Sciences — Sparking Ideas —

      • Michael R. Berthold

      Pages 1-1

   2. Machine Learning for Natural Language Processing (and Vice Versa?)

      • Claire Cardie

      Pages 2-2

   3. Statistical Relational Learning: An Inductive Logic Programming Perspective

      • Luc De Raedt

      Pages 3-5

   4. Recent Advances in Mining Time Series Data

      • Eamonn Keogh

      Pages 6-6

   5. Focus the Mining Beacon: Lessons and Challenges from the World of E-Commerce

      • Ron Kohavi

      Pages 7-7

   6. Data Streams and Data Synopses for Massive Data Sets

      • Yossi Matias

      Pages 8-9

3. Long Papers

   1. k-Anonymous Patterns

      • Maurizio Atzori, Francesco Bonchi, Fosca Giannotti, Dino Pedreschi

      Pages 10-21

   2. Interestingness is Not a Dichotomy: Introducing Softness in Constrained Pattern Mining

      • Stefano Bistarelli, Francesco Bonchi

      Pages 22-33

   3. Generating Dynamic Higher-Order Markov Models in Web Usage Mining

      • José Borges, Mark Levene

      Pages 34-45

   4. Tree ² – Decision Trees for Tree Structured Data

      • Björn Bringmann, Albrecht Zimmermann

      Pages 46-58

   5. Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results

      • Ian Davidson, S. S. Ravi

      Pages 59-70

   6. Cluster Aggregate Inequality and Multi-level Hierarchical Clustering

      • Chris Ding, Xiaofeng He

      Pages 71-83

   7. Ensembles of Balanced Nested Dichotomies for Multi-class Problems

      • Lin Dong, Eibe Frank, Stefan Kramer

      Pages 84-95

   8. Protein Sequence Pattern Mining with Constraints

      • Pedro Gabriel Ferreira, Paulo J. Azevedo

      Pages 96-107

   9. An Adaptive Nearest Neighbor Classification Algorithm for Data Streams

      • Yan-Nei Law, Carlo Zaniolo

      Pages 108-120

   10. Support Vector Random Fields for Spatial Classification

       • Chi-Hoon Lee, Russell Greiner, Mark Schmidt

       Pages 121-132

   11. Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication

       • Jurij Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos

       Pages 133-145

   12. A Correspondence Between Maximal Complete Bipartite Subgraphs and Closed Patterns

       • Jinyan Li, Haiquan Li, Donny Soh, Limsoon Wong

       Pages 146-156

   13. Improving Generalization by Data Categorization

       • Ling Li, Amrit Pratap, Hsuan-Tien Lin, Yaser S. Abu-Mostafa

       Pages 157-168

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.

• 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

• LIACC/FEP, Universidade do Porto, Portugal

  Alípio Mário Jorge

• LIAAD-INESC Porto LA / FEP, University of Porto, Porto, Portugal

  Luís Torgo

• LIAAD-INESC Porto L.A./Faculty of Economics, University of Porto, Porto, Portugal

  Pavel Brazdil

• Faculdade de Engenharia & LIAAD, Universidade do Porto, Portugal

  Rui Camacho

• Faculty of Economics of the University of Porto, Portugal

  João Gama

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