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.

This book constitutes the refereed proceedings of the twelfth International Conference, on Discovery Science, DS 2009, held in Porto, Portugal, in October 2009. The 35 revised full papers presented were carefully selected from 92 papers. The scope of the conference includes the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their applications.