Discovery Science

Invited Papers.- On Iterative Algorithms with an Information Geometry Background.- Visual Analytics: Combining Automated Discovery with Interactive Visualizations.- Some Mathematics Behind Graph Property Testing.- Finding Total and Partial Orders from Data for Seriation.- Computational Models of Neural Representations in the Human Brain.- Learning.- Unsupervised Classifier Selection Based on Two-Sample Test.- An Empirical Investigation of the Trade-Off between Consistency and Coverage in Rule Learning Heuristics.- Learning Model Trees from Data Streams.- Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees.- Ensemble-Trees: Leveraging Ensemble Power Inside Decision Trees.- A Comparison between Neural Network Methods for Learning Aggregate Functions.- Feature Selection.- Smoothed Prediction of the Onset of Tree Stem Radius Increase Based on Temperature Patterns.- Feature Selection in Taxonomies with Applications to Paleontology.- Associations.- Deduction Schemes for Association Rules.- Constructing Iceberg Lattices from Frequent Closures Using Generators.- Discovery Processes.- Learning from Each Other.- Comparative Evaluation of Two Systems for the Visual Navigation of Encyclopedia Knowledge Spaces.- A Framework for Knowledge Discovery in a Society of Agents.- Learning and Chemistry.- Active Learning for High Throughput Screening.- An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules.- Mining Intervals of Graphs to Extract Characteristic Reaction Patterns.- Clustering.- Refining Pairwise Similarity Matrix for Cluster Ensemble Problem with Cluster Relations.- Input Noise Robustness and Sensitivity Analysis to Improve Large Datasets Clustering by Using the GRID.- An Integrated Graph and Probability Based Clustering Framework for Sequential Data.- Cluster Analysis in Remote Sensing Spectral Imagery through Graph Representation and Advanced SOM Visualization.- Structured Data.- Mining Unordered Distance-Constrained Embedded Subtrees.- Finding Frequent Patterns from Compressed Tree-Structured Data.- A Modeling Approach Using Multiple Graphs for Semi-Supervised Learning.- Text Analysis.- String Kernels Based on Variable-Length-Don't-Care Patterns.- Unsupervised Spam Detection by Document Complexity Estimation.- A Probabilistic Neighbourhood Translation Approach for Non-standard Text Categorisation.