Principles of data mining and knowledge discovery

About this book

Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD 2000 Lyon, France, September 13–16, 2000 Proceedings Author: Djamel A. Zighed, Jan Komorowski, Jan Żytkow Published by Springer Berlin Heidelberg ISBN: 978-3-540-41066-9 DOI: 10.1007/3-540-45372-5 Table of Contents: 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 Confirmation Rule Sets Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery Combining Multiple Models with Meta Decision Trees 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 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 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 Classification by Emerging Patterns Context-Based Similarity Measures for Categorical Databases