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
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