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