Discovery Science

1. Front Matter

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2. Mixture Models from Multiresolution 0-1 Data

   • Prem Raj Adhikari, Jaakko Hollmén

   Pages 1-16

3. Model Tree Ensembles for Modeling Dynamic Systems

   • Darko Aleksovski, Juš Kocijan, Sašo Džeroski

   Pages 17-32

4. Fast and Scalable Image Retrieval Using Predictive Clustering Trees

   • Ivica Dimitrovski, Dragi Kocev, Suzana Loskovska, Sašo Džeroski

   Pages 33-48

5. Avoiding Anomalies in Data Stream Learning

   • João Gama, Petr Kosina, Ezilda Almeida

   Pages 49-63

6. Generalizing from Example Clusters

   • Pan Hu, Celine Vens, Bart Verstrynge, Hendrik Blockeel

   Pages 64-78

7. Clustering Based Active Learning for Evolving Data Streams

   • Dino Ienco, Albert Bifet, Indrė Žliobaitė, Bernhard Pfahringer

   Pages 79-93

8. Robust Crowd Labeling Using Little Expertise

   • Faiza Khan Khattak, Ansaf Salleb-Aouissi

   Pages 94-109

9. A New Approach to String Pattern Mining with Approximate Match

   • Tetsushi Matsui, Takeaki Uno, Juzoh Umemori, Tsuyoshi Koide

   Pages 110-125

10. OntoDM-KDD: Ontology for Representing the Knowledge Discovery Process

    • Panče Panov, Larisa Soldatova, Sašo Džeroski

    Pages 126-140

11. A Wordification Approach to Relational Data Mining

    • Matic Perovšek, Anže Vavpetič, Bojan Cestnik, Nada Lavrač

    Pages 141-154

12. Multi-interval Discretization of Continuous Attributes for Label Ranking

    • Cláudio Rebelo de Sá, Carlos Soares, Arno Knobbe, Paulo Azevedo, Alípio Mário Jorge

    Pages 155-169

13. Identifying Super-Mediators of Information Diffusion in Social Networks

    • Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda

    Pages 170-184

14. SM2D: A Modular Knowledge Discovery Approach Applied to Hydrological Forecasting

    • Wilfried Segretier, Martine Collard

    Pages 185-200

15. A Dynamic Programming Algorithm for Learning Chain Event Graphs

    • Tomi Silander, Tze-Yun Leong

    Pages 201-216

16. Mining Interesting Patterns in Multi-relational Data with N-ary Relationships

    • Eirini Spyropoulou, Tijl De Bie, Mario Boley

    Pages 217-232

17. Learning Hierarchical Multi-label Classification Trees from Network Data

    • Daniela Stojanova, Michelangelo Ceci, Donato Malerba, Sašo Džeroski

    Pages 233-248

18. A Density-Based Backward Approach to Isolate Rare Events in Large-Scale Applications

    • Enikö Székely, Pascal Poncelet, Florent Masseglia, Maguelonne Teisseire, Renaud Cezar

    Pages 249-264

19. Inductive Process Modeling of Rab5-Rab7 Conversion in Endocytosis

    • Jovan Tanevski, Ljupčo Todorovski, Yannis Kalaidzidis, Sašo Džeroski

    Pages 265-280

20. Fast Compression of Large-Scale Hypergraphs for Solving Combinatorial Problems

    • Takahisa Toda

    Pages 281-293

This book constitutes the proceedings of the 16th International Conference on Discovery Science, DS 2013, held in Singapore in October 2013, and co-located with the International Conference on Algorithmic Learning Theory, ALT 2013. The 23 papers presented in this volume were carefully reviewed and selected from 52 submissions. They cover recent advances in the development and analysis of methods of automatic scientific knowledge discovery, machine learning, intelligent data analysis, and their application to knowledge discovery.

• constraint-based clustering
• domain ontology
• hypernetworks
• semantic data mining
• structure learning
• algorithm analysis and problem complexity

• TU Darmstadt, Germany

  Johannes Fürnkranz

• Phillips-Universität Marburg, Germany

  Eyke Hüllermeier

• The Institute of Statistical Mathematics, Tokyo, Japan

  Tomoyuki Higuchi

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