Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD, 2000, Lyon, France, September 13-16, 2000 Proceedings

Towards Broader Foundations.- Multi-relational Data Mining, Using UML for ILP.- An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data.- Basis of a Fuzzy Knowledge Discovery System.- Rules and Trees.- Confirmation Rule Sets.- Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery.- Combining Multiple Models with Meta Decision Trees.- Databases and Reward-Based Learning.- Materialized Data Mining Views.- Approximation of Frequency Queries by Means of Free-Sets.- Application of Reinforcement Learning to Electrical Power System Closed-Loop Emergency Control.- Efficient Score-Based Learning of Equivalence Classes of Bayesian Networks.- Classication.- Quantifying the Resilience of Inductive Classification Algorithms.- Bagging and Boosting with Dynamic Integration of Classifiers.- Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information.- Some Enhancements of Decision Tree Bagging.- Association Rules and Exceptions.- Relative Unsupervised Discretization for Association Rule Mining.- Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today’s Approaches.- Unified Algorithm for Undirected Discovery of Exception Rules.- Sampling Strategies for Targeting Rare Groups from a Bank Customer Database.- Instance-Based Discovery.- Instance-Based Classification by Emerging Patterns.- Context-Based Similarity Measures for Categorical Databases.- A Mixed Similarity Measure in Near-Linear Computational Complexity for Distance-Based Methods.- Fast Feature Selection using Partial Correlation for Multi-valued Attributes.- Clustering and Classification.- Fast Hierarchical Clustering Based on Compressed Data and OPTICS.- Accurate Recasting of Parameter Estimation Algorithms Using Sufficient Statistics for Efficient Parallel Speed-Up.- Predictive Performance of Weighted Relative Accuracy.- Quality Scheme Assessment in the Clustering Process.- Time Series.- Algorithm for Matching Sets of Time Series.- MSTS: A System for Mining Sets of Time Series.- Learning First Order Logic Time Series Classifiers: Rules and Boosting.- Posters.- Learning Right Sized Belief Networks by Means of a Hybrid Methodology.- Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets.- Discovering Task Neighbourhoods through Landmark Learning Performances.- Induction of Multivariate Decision Trees by Using Dipolar Criteria.- Inductive Logic Programming in Clementine.- A Genetic Algorithm-Based Solution for the Problem of Small Disjuncts.- Clustering Large, Multi-level Data Sets: An Approach Based on Kohonen Self Organizing Maps.- Trees and Induction Graphs for Multivariate Response.- CEM - A Program for Visualization and Discovery in Email.- Image Access and Data Mining: An Approach.- Decision Tree Toolkit: A Component-Based Library of Decision Tree Algorithms.- Determination of Screening Descriptors for Chemical Reaction Databases.- Prior Knowledge in Economic Applications of Data Mining.- Temporal Machine Learning for Switching Control.- Improving Dissimilarity Functions with Domain Knowledge, applications with IKBS system.- Mining Weighted Association Rules for Fuzzy Quantitative Items.- Centroid-Based Document Classification: Analysis and Experimental Results.- Applying Objective Interestingness Measures in Data Mining Systems.- Observational Logic Integrates Data Mining Based on Statistics and Neural Networks.- Supporting Discovery in Medicine by Association Rule Mining of Bibliographic Databases.- Collective Principal Component Analysis from Distributed, Heterogeneous Data.- Hierarchical Document Clustering Based on Tolerance Rough Set Model.- Application of Data-Mining and Knowledge Discovery in Automotive Data Engineering.- Towards Knowledge Discovery from cDNA Microarray Gene Expression Data.- Mining with Cover and Extension Operators.- A User-Driven Process for Mining Association Rules.- Learning from Labeled and Unlabeled Documents: A Comparative Study on Semi-Supervised Text Classification.- Schema Mining: Finding Structural Regularity among Semistructured Data.- Improving an Association Rule Based Classifier.- Discovery of Generalized Association Rules with Multiple Minimum Supports.- Learning Dynamic Bayesian Belief Networks Using Conditional Phase-Type Distributions.- Discovering Differences in Patients with Uveitis through Typical Testors by Class.- Web Usage Mining: How to Efficiently Manage New Transactions and New Clients.- Mining Relational Databases.- Interestingness in Attribute-Oriented Induction (AOI): Multiple-Level Rule Generation.- Discovery of Characteristic Subgraph Patterns Using Relative Indexing and the Cascade Model.- Transparency and Predictive Power.- Clustering Distributed Homogeneous Datasets.- Empirical Evaluation of Feature Subset Selection Based on a Real-World Data Set.- Discovery of Ambiguous Patterns in Sequences Application to Bioinformatics.- Action-Rules: How to Increase Profit of a Company.- Aggregation and Association in Cross Tables.- An Experimental Study of Partition Quality Indices in Clustering.- Expert Constrained Clustering: A Symbolic Approach.- An Application of Association Rules Discovery to Geographic Information Systems.- Generalized Entropy and Projection Clustering of Categorical Data.- Supporting Case Acquisition and Labelling in the Context of Web Mining.- Indirect Association: Mining Higher Order Dependencies in Data.- Discovering Association Rules in Large, Dense Databases.- Providing Advice to Website Designers Towards Effective Websites Re-organization.- Clinical Knowledge Discovery in Hospital Information Systems: Two Case Studies.- Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case.- Lightweight Document Clustering.- Automatic Category Structure Generation and Categorization of Chinese Text Documents.- Mining Generalized Multiple-level Association Rules.- An Efficient Approach to Discovering Sequential Patterns in Large Databases.- Using Background Knowledge as a Bias to Control the Rule Discovery Process.