Discovery Science

1. Front Matter

   PDF

2. Invited Talks

   1. Declarative Modeling for Machine Learning and Data Mining

      • Luc De Raedt

      Pages 1-1

   2. Recent Developments in Pattern Mining

      • Toon Calders

      Pages 2-2

3. Tutorial

   1. Exploring Sequential Data

      • Gilbert Ritschard

      Pages 3-6

4. Regular Papers

   1. Large Scale Spectral Clustering Using Resistance Distance and Spielman-Teng Solvers

      • Nguyen Lu Dang Khoa, Sanjay Chawla

      Pages 7-21

   2. Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting

      • Pierre Alquier, Xiaoyin Li

      Pages 22-36

   3. Policy Search in a Space of Simple Closed-form Formulas: Towards Interpretability of Reinforcement Learning

      • Francis Maes, Raphael Fonteneau, Louis Wehenkel, Damien Ernst

      Pages 37-51

   4. Towards Finding Relational Redescriptions

      • Esther Galbrun, Angelika Kimmig

      Pages 52-66

   5. Descriptive Modeling of Systemic Banking Crises

      • Dragan Gamberger, Dražen Lučanin, Tomislav Šmuc

      Pages 67-80

   6. A Trim Distance between Positions in Nucleotide Sequences

      • Shunsuke Makino, Takaharu Shimada, Kouichi Hirata, Kouki Yonezawa, Kimihito Ito

      Pages 81-94

   7. Data Squashing for HSV Subimages by an Autonomous Mobile Robot

      • Einoshin Suzuki, Emi Matsumoto, Asuki Kouno

      Pages 95-109

   8. Cohesive Co-evolution Patterns in Dynamic Attributed Graphs

      • Elise Desmier, Marc Plantevit, Céline Robardet, Jean-François Boulicaut

      Pages 110-124

   9. Efficient Redundancy Reduced Subgroup Discovery via Quadratic Programming

      • Rui Li, Stefan Kramer

      Pages 125-138

   10. HCAC: Semi-supervised Hierarchical Clustering Using Confidence-Based Active Learning

       • Bruno M. Nogueira, Alípio M. Jorge, Solange O. Rezende

       Pages 139-153

   11. LF-CARS: A Loose Fragment-Based Consensus Clustering Algorithm with a Robust Similarity

       • Bi-Ru Dai, Chih-Heng Chung

       Pages 154-168

   12. Fast Approximation Algorithm for the 1-Median Problem

       • Koji Tabata, Atsuyoshi Nakamura, Mineichi Kudo

       Pages 169-183

   13. Online Co-regularized Algorithms

       • Tom de Ruijter, Evgeni Tsivtsivadze, Tom Heskes

       Pages 184-193

   14. Fast Progressive Training of Mixture Models for Model Selection

       • Prem Raj Adhikari, Jaakko Hollmén

       Pages 194-208

   15. Including Spatial Relations and Scales within Sequential Pattern Extraction

       • Mickaël Fabrègue, Agnès Braud, Sandra Bringay, Florence Le Ber, Maguelonne Teisseire

       Pages 209-223

   16. Predicting Ramp Events with a Stream-Based HMM Framework

       • Carlos Abreu Ferreira, João Gama, Vítor Santos Costa, Vladimiro Miranda, Audun Botterud

       Pages 224-238

This book constitutes the refereed proceedings of the 15th International Conference on Discovery Science, DS 2012, held in Lyon, France, in October 2012. The 22 papers presented in this volume were carefully reviewed and selected from 46 submissions. The field of discovery science aims at inducing and validating new scientific hypotheses from data. The scope of this conference includes the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, tools for supporting the human process of discovery in science, as well as their application to knowledge discovery.

• XML mining
• autonomous exploration
• data mining
• huge network
• reinforcement learning
• algorithm analysis and problem complexity

• LIP6 laboratory, Parix VI University, Paris, France

  Jean-Gabriel Ganascia

• Telecom Bretagne, UMR 6285 Lab-STICC, Brest Cedex 3, France

  Philippe Lenca

• INSA Lyon, UMR 5205 LIRIS, Villeurbanne Cedex, France

  Jean-Marc Petit

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