Principles of data mining and knowledge discovery

Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD 2000 Lyon, France, September 13–16, 2000 Proceedings<br />Author: Djamel A. Zighed, Jan Komorowski, Jan Żytkow<br /> Published by Springer Berlin Heidelberg<br /> ISBN: 978-3-540-41066-9<br /> DOI: 10.1007/3-540-45372-5<br /><br />Table of Contents:<p></p><ul><li>Multi-relational Data Mining, Using UML for ILP </li><li>An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data </li><li>Basis of a Fuzzy Knowledge Discovery System </li><li>Confirmation Rule Sets </li><li>Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery </li><li>Combining Multiple Models with Meta Decision Trees </li><li>Materialized Data Mining Views </li><li>Approximation of Frequency Queries by Means of Free-Sets </li><li>Application of Reinforcement Learning to Electrical Power System Closed-Loop Emergency Control </li><li>Efficient Score-Based Learning of Equivalence Classes of Bayesian Networks </li><li>Quantifying the Resilience of Inductive Classification Algorithms </li><li>Bagging and Boosting with Dynamic Integration of Classifiers </li><li>Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information </li><li>Some Enhancements of Decision Tree Bagging </li><li>Relative Unsupervised Discretization for Association Rule Mining </li><li>Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today’s Approaches </li><li>Unified Algorithm for Undirected Discovery of Exception Rules </li><li>Sampling Strategies for Targeting Rare Groups from a Bank Customer Database </li><li>Instance-Based Classification by Emerging Patterns </li><li>Context-Based Similarity Measures for Categorical Databases</li></ul>