Principles of Data Mining and Knowledge Discovery -

Résumé :
We are pleased to present the proceedings of the 13th European Conference on Machine Learning (LNAI 2430) and the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (LNAI 2431). These two c- ferences were colocated in Helsinki, Finland during August 19?23, 2002. ECML and PKDD were held together for the second year in a row, following the success of the colocation in Freiburg in 2001. Machine learning and knowledge discovery are two highly related ?elds and ECML/PKDD is a unique forum to foster their collaboration. The bene?t of colocation to both the machine learning and data mining communities is most clearly displayed in the common workshop, tutorial, and invited speaker program. Altogether six workshops and six tutorials were or- nized on Monday and Tuesday. As invited speakers we had the pleasure to have Erkki Oja (Helsinki Univ. of Technology), Dan Roth (Univ. of Illinois, Urbana- Champaign), Bernhard Sch?olkopf (Max Planck Inst. for Biological Cybernetics, T?ubingen), and Padhraic Smyth (Univ. of California, Irvine). The main events ran from Tuesday until Friday, comprising 41 ECML te- nical papers and 39 PKDD papers. In total, 218 manuscripts were submitted to these two conferences: 95 to ECML, 70 to PKDD, and 53 as joint submissions. All papers were assigned at least three reviewers from our international program committees. Out of the 80 accepted papers 31 were ?rst accepted conditi- ally; the revised manuscripts were accepted only after the conditions set by the reviewers had been met.

Sommaire:
Contributed Papers.- Optimized Substructure Discovery for Semi-structured Data.- Fast Outlier Detection in High Dimensional Spaces.- Data Mining in Schizophrenia Research - Preliminary Analysis.- Fast Algorithms for Mining Emerging Patterns.- On the Discovery of Weak Periodicities in Large Time Series.- The Need for Low Bias Algorithms in Classification Learning from Large Data Sets.- Mining All Non-derivable Frequent Itemsets.- Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance.- Finding Association Rules with Some Very Frequent Attributes.- Unsupervised Learning: Self-aggregation in Scaled Principal Component Space*.- A Classification Approach for Prediction of Target Events in Temporal Sequences.- Privacy-Oriented Data Mining by Proof Checking.- Choose Your Words Carefully: An Empirical Study of Feature Selection Metrics for Text Classification.- Generating Actionable Knowledge by Expert-Guided Subgroup Discovery.- Clustering Transactional Data.- Multiscale Comparison of Temporal Patterns in Time-Series Medical Databases.- Association Rules for Expressing Gradual Dependencies.- Support Approximations Using Bonferroni-Type Inequalities.- Using Condensed Representations for Interactive Association Rule Mining.- Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting.- Dependency Detection in MobiMine and Random Matrices.- Long-Term Learning for Web Search Engines.- Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database.- Involving Aggregate Functions in Multi-relational Search.- Information Extraction in Structured Documents Using Tree Automata Induction.- Algebraic Techniques for Analysis of Large Discrete-Valued Datasets.- Geography of Di.erences between Two Classes of Data.- Rule Induction for Classification of Gene Expression Array Data.- Clustering Ontology-Based Metadata in the Semantic Web.- Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases.- SVMClassification Using Sequences of Phonemes and Syllables.- A Novel Web Text Mining Method Using the Discrete Cosine Transform.- A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases.- Answering the Most Correlated N Association Rules Efficiently.- Mining Hierarchical Decision Rules from Clinical Databases Using Rough Sets and Medical Diagnostic Model.- Efficiently Mining Approximate Models of Associations in Evolving Databases.- Explaining Predictions from a Neural Network Ensemble One at a Time.- Structuring Domain-Specific Text Archives by Deriving a Probabilistic XML DTD.- Separability Index in Supervised Learning.- Invited Papers.- Finding Hidden Factors Using Independent Component Analysis.- Reasoning with Classifiers*.- A Kernel Approach for Learning from Almost Orthogonal Patterns.- Learning with Mixture Models: Concepts and Applications.