This book constitutes the refereed proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with ECML 2003. The 40 revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another 40 ones for ECML 2003, selected from a total of 332 submissions. The papers address all current issues in data mining and knowledge discovery in databases including data mining tools, association rule mining, classification, clustering, pattern mining, multi-relational classifiers, boosting, kernel methods, learning Bayesian networks, inductive logic programming, user preferences mining, time series analysis, multi-view learning, support vector machine, pattern mining, relational learning, categorization, information extraction, decision making, prediction, and decision trees.

• Invited Papers.- From Knowledge-Based to Skill-Based Systems: Sailing as a Machine Learning Challenge.- Two-Eyed Algorithms and Problems.- Next Generation Data Mining Tools: Power Laws and Self-similarity for Graphs, Streams and Traditional Data.- Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions.- Contributed Papers.- Efficient Statistical Pruning of Association Rules.- Majority Classification by Means of Association Rules.- Adaptive Constraint Pushing in Frequent Pattern Mining.- ExAnte: Anticipated Data Reduction in Constrained Pattern Mining.- Minimal k-Free Representations of Frequent Sets.- Discovering Unbounded Episodes in Sequential Data.- Mr-SBC: A Multi-relational Naïve Bayes Classifier.- SMOTEBoost: Improving Prediction of the Minority Class in Boosting.- Using Belief Networks and Fisher Kernels for Structured Document Classification.- A Skeleton-Based Approach to Learning Bayesian Networks from Data.- On Decision Boundaries of Naïve Bayes in Continuous Domains.- Application of Inductive Logic Programming to Structure-Based Drug Design.- Visualizing Class Probability Estimators.- Automated Detection of Epidemics from the Usage Logs of a Physicians’ Reference Database.- An Indiscernibility-Based Clustering Method with Iterative Refinement of Equivalence Relations.- Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications.- Explaining Text Clustering Results Using Semantic Structures.- Analyzing Attribute Dependencies.- Ranking Interesting Subspaces for Clustering High Dimensional Data.- Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases.- Using Transduction and Multi-view Learning to Answer Emails.- Exploring Fringe Settings of SVMs for Classification.- Rule Discovery and Probabilistic Modeling for Onomastic Data.- Constraint-Based Mining of Sequential Patterns over Datasets with Consecutive Repetitions.- Symbolic Distance Measurements Based on Characteristic Subspaces.- The Pattern Ordering Problem.- Collaborative Filtering Using Restoration Operators.- Efficient Frequent Query Discovery in Farmer.- Towards Behaviometric Security Systems: Learning to Identify a Typist.- Efficient Density Clustering Method for Spatial Data.- Statistical ?-Partition Clustering over Data Streams.- Enriching Relational Learning with Fuzzy Predicates.- Text Categorisation Using Document Profiling.- A Simple Algorithm for Topic Identification in 0–1 Data.- Bottom-Up Learning of Logic Programs for Information Extraction from Hypertext Documents.- Predicting Outliers.- Mining Rules of Multi-level Diagnostic Procedure from Databases.- Learning Characteristic Rules Relying on Quantified Paths.- Topic Learning from Few Examples.- Arbogodaï, a New Approach for Decision Trees.