Session 1A - Time Series -- Scaling up Dynamic Time Warping to Massive Datasets -- The Haar Wavelet Transform in the Time Series Similarity Paradigm -- Rule Discovery in Large Time-Series Medical Databases -- Session 1B - Applications -- Simultaneous Prediction of Multiple Chemical Parameters of River Water Quality with TILDE -- Applying Data Mining Techniques to Wafer Manufacturing -- An Application of Data Mining to the Problem of the University Students' Dropout Using Markov Chains -- Session 2A - Taxonomies and Partitions -- Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD -- Taxonomy Formation by Approximate Equivalence Relations, Revisited -- On the Use of Self-Organizing Maps for Clustering and Visualization -- Speeding Up the Search for Optimal Partitions -- Session 2B - Logic Methods -- Experiments in Meta-level Learning with ILP -- Boolean Reasoning Scheme with Some Applications in Data Mining -- On the Correspondence between Classes of Implicational and Equivalence Quantifiers -- Querying Inductive Databases via Logic-Based User-Defined Aggregates -- Session 3A - Distributed and Multirelational Databases -- Peculiarity Oriented Multi-database Mining -- Knowledge Discovery in Medical Multi-databases: A Rough Set Approach -- Automated Discovery of Rules and Exceptions from Distributed Databases Using Aggregates -- Session 3B - Text Mining and Feature Selection -- Text Mining via Information Extraction -- TopCat: Data Mining for Topic Identification in a Text Corpus -- Selection and Statistical Validation of Features and Prototypes -- Session 4A - Rules and Induction -- Taming Large Rule Models in Rough Set Approaches -- Optimizing Disjunctive Association Rules -- Contribution of Boosting in Wrapper Models -- Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme -- Session 5A - Interesting and Unusual -- Heuristic Measures of Interestingness -- Enhancing Rule Interestingness for Neuro-fuzzy Systems -- Unsupervised Profiling for Identifying Superimposed Fraud -- OPTICS-OF: Identifying Local Outliers -- Posters -- Selective Propositionalization for Relational Learning -- Circle Graphs: New Visualization Tools for Text-Mining -- On the Consistency of Information Filters for Lazy Learning Algorithms -- Using Genetic Algorithms to Evolve a Rule Hierarchy -- Mining Temporal Features in Association Rules -- The Improvement of Response Modeling: Combining Rule-Induction and Case-Based Reasoning -- Analyzing an Email Collection Using Formal Concept Analysis -- Business Focused Evaluation Methods: A Case Study -- Combining Data and Knowledge by MaxEnt-Optimization of Probability Distributions -- Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation? -- Rough Dependencies as a Particular Case of Correlation: Application to the Calculation of Approximative Reducts -- A Fuzzy Beam-Search Rule Induction Algorithm -- An Innovative GA-Based Decision Tree Classifier in Large Scale Data Mining -- Extension to C-means Algorithm for the Use of Similarity Functions -- Predicting Chemical Carcinogenesis Using Structural Information Only -- LA - A Clustering Algorithm with an Automated Selection of Attributes, Which is Invariant to Functional Transformations of Coordinates -- Association Rule Selection in a Data Mining Environment -- Multi-relational Decision Tree Induction -- Learning of Simple Conceptual Graphs from Positive and Negative Examples -- An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction -- ZigZag, a New Clustering Algorithm to Analyze Categorical Variable Cross-Classification Tables -- Efficient Mining of High Confidence Association Rules without Support Thresholds -- A Logical Approach to Fuzzy Data Analysis -- AST: Support for Algorithm Selection with a CBR Approach -- Efficient Shared Near Neighbours Clustering of Large Metric Data Sets -- Discovery of "Interesting" Data Dependencies from a Workload of SQL Statements -- Learning from Highly Structured Data by Decomposition -- Combinatorial Approach for Data Binarization -- Extending Attribute-Oriented Induction as a Key-Preserving Data Mining Method -- Automated Discovery of Polynomials by Inductive Genetic Programming -- Diagnosing Acute Appendicitis with Very Simple Classification Rules -- Rule Induction in Cascade Model Based on Sum of Squares Decomposition -- Maintenance of Discovered Knowledge -- A Divisive Initialisation Method for Clustering Algorithms -- A Comparison of Model Selection Procedures for Predicting Turning Points in Financial Time Series -- Mining Lemma Disambiguation Rules from Czech Corpora -- Adding Temporal Semantics to Association Rules -- Studying the Behavior of Generalized Entropy in Induction Trees Using a M-of-N Concept -- Discovering Rules in Information Trees -- Mining Text Archives: Creating Readable Maps to Structure and Describe Document Collections -- Neuro-fuzzy Data Mining for Target Group Selection in Retail Banking -- Mining Possibilistic Set-Valued Rules by Generating Prime Disjunctions -- Towards Discovery of Information Granules -- Classification Algorithms Based on Linear Combinations of Features -- Managing Interesting Rules in Sequence Mining -- Support Vector Machines for Knowledge Discovery -- Regression by Feature Projections -- Generating Linguistic Fuzzy Rules for Pattern Classification with Genetic Algorithms -- Tutorials -- Data Mining for Robust Business Intelligence Solutions -- Query Languages for Knowledge Discovery in Databases -- The ESPRIT Project CreditMine and its Relevance for the Internet Market -- Logics and Statistics for Association Rules and Beyond -- Data Mining for the Web -- Relational Learning and Inductive Logic Programming Made Easy.