Principles of Data Mining and Knowledge Discovery

"This book constitutes the refereed proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2001, held in Freiburg, Germany, in September 2001.
The 40 revised full papers presented together with four invited contributions were carefully reviewed and selected from close to 100 submissions. Among the topics addressed are hidden Markov models, text summarization, supervised learning, unsupervised learning, demographic data analysis, phenotype data mining, spatio-temporal clustering, Web-usage analysis, association rules, clustering algorithms, time series analysis, rule discovery, text categorization, self-organizing maps, filtering, reinforcemant learning, support vector machines, visual data mining, and machine learning. "

Regular Paters
Self Similar Layered Hidden Markov Models
Automatic Text Summarization Using Unsupervised and Semi-supervised Learning
Detecting Temporal Changes in Event Sequences:An Application to Demographic Data
Knowledge Discovery in Multi-label Phenotype Data
Computing Association Rules Using Partial Totals
Gaphyl: A Genetic Algorithms Apporoach ot Cladistics
Parametric Approximation Algorithms for High-Dimensional Euclidean Similarity
Data Structures for Minmization of Total Within-Group Distance for Spatio-Temporal Clustering
Non-crisp Clustering by Fast,Convergent,and Robust Algorithms
Pattern Extraction for Time Series Classification
Specifyling Mining Algorithms with Iterative User-Defined Aggregates:A Case Study
Interesting Fuzzy Assiciation Rules in Quantitave Databases
Interestingness Measures for Fuzzy Association Rules
A Data Set Oriented Approach for Clustering Algorithm Selection
Fusion of Meta-knowledge and Meta-data for Case-Based Model Selection
Discovery of Temporal Patterns.Learning Rules about the Qualitative
Behaviour of Time Series
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