Advances in Intelligent Data Analysis VI

1. Front Matter

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2. Probabilistic Latent Clustering of Device Usage

   • Jean-Marc Andreoli, Guillaume Bouchard

   Pages 1-11

3. Condensed Nearest Neighbor Data Domain Description

   • Fabrizio Angiulli

   Pages 12-23

4. Balancing Strategies and Class Overlapping

   • Gustavo E. A. P. A. Batista, Ronaldo C. Prati, Maria C. Monard

   Pages 24-35

5. Modeling Conditional Distributions of Continuous Variables in Bayesian Networks

   • Barry R. Cobb, Rafael Rumí, Antonio Salmerón

   Pages 36-45

6. Kernel K-Means for Categorical Data

   • Julia Couto

   Pages 46-56

7. Using Genetic Algorithms to Improve Accuracy of Economical Indexes Prediction

   • Óscar Cubo, Víctor Robles, Javier Segovia, Ernestina Menasalvas

   Pages 57-65

8. A Distance-Based Method for Preference Information Retrieval in Paired Comparisons

   • Esther Dopazo, Jacinto González-Pachón, Juan Robles

   Pages 66-73

9. Knowledge Discovery in the Identification of Differentially Expressed Genes in Tumoricidal Macrophage

   • A. Fazel Famili, Ziying Liu, Pedro Carmona-Saez, Alaka Mullick

   Pages 74-85

10. Searching for Meaningful Feature Interactions with Backward-Chaining Rule Induction

    • Doug Fisher, Mary Edgerton, Lianhong Tang, Lewis Frey, Zhihua Chen

    Pages 86-96

11. Exploring Hierarchical Rule Systems in Parallel Coordinates

    • Thomas R. Gabriel, A. Simona Pintilie, Michael R. Berthold

    Pages 97-108

12. Bayesian Networks Learning for Gene Expression Datasets

    • Giacomo Gamberoni, Evelina Lamma, Fabrizio Riguzzi, Sergio Storari, Stefano Volinia

    Pages 109-120

13. Pulse: Mining Customer Opinions from Free Text

    • Michael Gamon, Anthony Aue, Simon Corston-Oliver, Eric Ringger

    Pages 121-132

14. Keystroke Analysis of Different Languages: A Case Study

    • Daniele Gunetti, Claudia Picardi, Giancarlo Ruffo

    Pages 133-144

15. Combining Bayesian Networks with Higher-Order Data Representations

    • Elias Gyftodimos, Peter A. Flach

    Pages 145-156

16. Removing Statistical Biases in Unsupervised Sequence Learning

    • Yoav Horman, Gal A. Kaminka

    Pages 157-167

17. Learning from Ambiguously Labeled Examples

    • Eyke Hüllermeier, Jürgen Beringer

    Pages 168-179

18. Learning Label Preferences: Ranking Error Versus Position Error

    • Eyke Hüllermeier, Johannes Fürnkranz

    Pages 180-191

19. FCLib: A Library for Building Data Analysis and Data Discovery Tools

    • Wendy S. Koegler, W. Philip Kegelmeyer

    Pages 192-203

20. A Knowledge-Based Model for Analyzing GSM Network Performance

    • Pasi Lehtimäki, Kimmo Raivio

    Pages 204-215

One of the superb characteristics of Intelligent Data Analysis (IDA) is that it is an interdisciplinary ?eld in which researchers and practitioners from a number of areas are involved in a typical project. This also creates a challenge in which the success of a team depends on the participation of users and domain experts who need to interact with researchers and developers of any IDA system. All this is usually re?ected in successful projects and of course on the papers that were evaluated by this year’s program committee from which the ?nal program has been developed. In our call for papers, we solicited papers on (i) applications and tools, (ii) theory and general principles, and (iii) algorithms and techniques. We received a total of 184 papers, reviewing these was a major challenge. Each paper was assigned to three reviewers. In the end 46 papers were accepted, which are all included in the proceedings and presented at the conference. This year’s papers re?ect the results of applied and theoretical researchfrom a number of disciplines all of which are related to the ?eld of Intelligent Data Analysis. To have the best combination of theoretical and applied research and also provide the best focus, we have divided this year’s IDA program into tu- rials, invited talks, panel discussions and technical sessions.

• Analysis
• classification
• cognition
• data mining
• learning
• machine learning
• modeling
• pattern recognition
• statistics
• visualization

• Institute for Information Technology, National Research Council Canada, Ottawa, Canada

  A. Fazel Famili

• LIACS, Leiden University, The Netherlands

  Joost N. Kok

• IFM, Linköping University, Linköping, Sweden

  José M. Peña

• Department of Computer Science, Universiteit Utrecht,  

  Arno Siebes

• Utrecht University, TB Utrecht,, the Netherlands

  Ad Feelders

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