Discovery Science

We are pleased to present the proceedings of the 12th International Conference on Discovery Science (DS 2009), held in Porto, Portugal, October 3 5, 2009. DS 2009 was collocated with ALT 2009, the 20th International Conference on AlgorithmicLearningTheory,continuingthesuccessfulDSconferenceseries. DS 2009 provided an open forum for intensive discussions and the exchange of new ideas among researchers working in the area of discovery science. The scope of the conference included the development and analysis of methods for automatic scienti?c knowledge discovery, machine learning, intelligent data analysis, and theory of learning, as well as their applications. We were honored to have a very strong program. Acceptance for the conference proceedings was very compe- tive. There were 92 papers submitted, with the authors coming from roughly 20 di?erent countries. All paperswere reviewedby three senior researchersfollowed by an extensive discussion. The program committee decided to accept 23 long papers (an acceptance rate of 25%) and 12 regular papers. The overall acc- tance rate was 38%. The contributed papers cover a wide range of topics, from discovery in general to data mining in particular. In addition to the technicalpapers, we weredelighted to have?ve prestigious invited speakers and two tutorials. Fernando Pereira, University of Penns- vania, USA, presented new fundamental questions that should be investigated in natural language processing in web mining. Hector Ge?ner, from Pompeu Fabra University, Spain, discussed learning methods for solving complete pl- ning domains.

Inference and Learning in Planning (Extended Abstract).- Mining Heterogeneous Information Networks by Exploring the Power of Links.- Learning on the Web.- Learning and Domain Adaptation.- The Two Faces of Active Learning.- An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting.- Detecting New Kinds of Patient Safety Incidents.- Using Data Mining for Wine Quality Assessment.- MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio.- On the Complexity of Constraint-Based Theory Extraction.- Algorithm and Feature Selection for VegOut: A Vegetation Condition Prediction Tool.- Regression Trees from Data Streams with Drift Detection.- Mining Frequent Bipartite Episode from Event Sequences.- CHRONICLE: A Two-Stage Density-Based Clustering Algorithm for Dynamic Networks.- Learning Large Margin First Order Decision Lists for Multi-Class Classification.- Centrality Measures from Complex Networks in Active Learning.- Player Modeling for Intelligent Difficulty Adjustment.- Unsupervised Fuzzy Clustering for the Segmentation and Annotation of Upwelling Regions in Sea Surface Temperature Images.- Discovering the Structures of Open Source Programs from Their Developer Mailing Lists.- A Comparison of Community Detection Algorithms on Artificial Networks.- Towards an Ontology of Data Mining Investigations.- OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers.- C-DenStream: Using Domain Knowledge on a Data Stream.- Discovering Influential Nodes for SIS Models in Social Networks.- An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules.- Precision and Recall for Regression.- Mining Local Correlation Patterns in Sets of Sequences.- Subspace Discovery for Promotion: A Cell Clustering Approach.- Contrasting Sequence Groups by Emerging Sequences.- A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams.- A Hybrid Collaborative Filtering System for Contextual Recommendations in Social Networks.- Linear Programming Boosting by Column and Row Generation.- Discovering Abstract Concepts to Aid Cross-Map Transfer for a Learning Agent.- A Dialectic Approach to Problem-Solving.- Gene Functional Annotation with Dynamic Hierarchical Classification Guided by Orthologs.- Stream Clustering of Growing Objects.- Finding the k-Most Abnormal Subgraphs from a Single Graph.- Latent Topic Extraction from Relational Table for Record Matching.- Computing a Comprehensible Model for Spam Filtering.- Better Decomposition Heuristics for the Maximum-Weight Connected Graph Problem Using Betweenness Centrality.