Discovery Science

1. Front Matter

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2. Explaining Mixture Models through Semantic Pattern Mining and Banded Matrix Visualization

   • Prem Raj Adhikari, Anže Vavpetič, Jan Kralj, Nada Lavrač, Jaakko Hollmén

   Pages 1-12

3. Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market

   • Alya Al Nasseri, Allan Tucker, Sergio de Cesare

   Pages 13-24

4. Synthetic Sequence Generator for Recommender Systems – Memory Biased Random Walk on a Sequence Multilayer Network

   • Nino Antulov-Fantulin, Matko Bošnjak, Vinko Zlatić, Miha Grčar, Tomislav Šmuc

   Pages 25-36

5. Predicting Sepsis Severity from Limited Temporal Observations

   • Xi Hang Cao, Ivan Stojkovic, Zoran Obradovic

   Pages 37-48

6. Completion Time and Next Activity Prediction of Processes Using Sequential Pattern Mining

   • Michelangelo Ceci, Pasqua Fabiana Lanotte, Fabio Fumarola, Dario Pietro Cavallo, Donato Malerba

   Pages 49-61

7. Antipattern Discovery in Ethiopian Bagana Songs

   • Darrell Conklin, Stéphanie Weisser

   Pages 62-72

8. Categorize, Cluster, and Classify: A 3-C Strategy for Scientific Discovery in the Medical Informatics Platform of the Human Brain Project

   • Tal Galili, Alexis Mitelpunkt, Netta Shachar, Mira Marcus-Kalish, Yoav Benjamini

   Pages 73-86

9. Multilayer Clustering: A Discovery Experiment on Country Level Trading Data

   • Dragan Gamberger, Matej Mihelčić, Nada Lavrač

   Pages 87-98

10. Medical Document Mining Combining Image Exploration and Text Characterization

    • Nicolau Gonçalves, Erkki Oja, Ricardo Vigário

    Pages 99-110

11. Mining Cohesive Itemsets in Graphs

    • Tayena Hendrickx, Boris Cule, Bart Goethals

    Pages 111-122

12. Mining Rank Data

    • Sascha Henzgen, Eyke Hüllermeier

    Pages 123-134

13. Link Prediction on the Semantic MEDLINE Network

    • Andrej Kastrin, Thomas C. Rindflesch, Dimitar Hristovski

    Pages 135-143

14. Medical Image Retrieval Using Multimodal Data

    • Ivan Kitanovski, Ivica Dimitrovski, Gjorgji Madjarov, Suzana Loskovska

    Pages 144-155

15. Fast Computation of the Tree Edit Distance between Unordered Trees Using IP Solvers

    • Seiichi Kondo, Keisuke Otaki, Madori Ikeda, Akihiro Yamamoto

    Pages 156-167

16. Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency

    • Georg Krempl, Daniel Kottke, Myra Spiliopoulou

    Pages 168-179

17. Incremental Learning with Social Media Data to Predict Near Real-Time Events

    • Duc Kinh Le Tran, Cécile Bothorel, Pascal Cheung Mon Chan, Yvon Kermarrec

    Pages 180-191

18. Stacking Label Features for Learning Multilabel Rules

    • Eneldo Loza Mencía, Frederik Janssen

    Pages 192-203

19. Selective Forgetting for Incremental Matrix Factorization in Recommender Systems

    • Pawel Matuszyk, Myra Spiliopoulou

    Pages 204-215

20. Providing Concise Database Covers Instantly by Recursive Tile Sampling

    • Sandy Moens, Mario Boley, Bart Goethals

    Pages 216-227

This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2014, held in Bled, Slovenia, in October 2014. The 30 full papers included in this volume were carefully reviewed and selected from 62 submissions. The papers cover topics such as: computational scientific discovery; data mining and knowledge discovery; machine learning and statistical methods; computational creativity; mining scientific data; data and knowledge visualization; knowledge discovery from scientific literature; mining text, unstructured and multimedia data; mining structured and relational data; mining temporal and spatial data; mining data streams; network analysis; discovery informatics; discovery and experimental workflows; knowledge capture and scientific ontologies; data and knowledge integration; logic and philosophy of scientific discovery; and applications of computational methods in various scientific domains.

• algorithms
• artificial intelligence
• data mining
• information retrieval
• intelligent data analysis
• knowledge discovery
• machine learning
• machine learning theory
• pattern mining
• recommendation systems
• recommender systems
• algorithm analysis and problem complexity

• Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia

  Sašo Džeroski, Panče Panov, Dragi Kocev

• Faculty of Administration, University of Ljubljana, Ljubljana, Slovenia

  Ljupčo Todorovski

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