Principles of Data Mining and Knowledge Discovery [electronic resource] : 5th European Conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001 Proceedings /

Regular Papers -- Self-Similar Layered Hidden Markov Models -- Automatic Text Summarization Using Unsupervised and Semi-supervised Learning -- Detecting Temporal Change 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 Approach to Cladistics -- Parametric Approximation Algorithms for High-Dimensional Euclidean Similarity -- Data Structures for Minimization of Total Within-Group Distance for Spatio-temporal Clustering -- Non-crisp Clustering by Fast, Convergent, and Robust Algorithms -- Pattern Extraction for Time Series Classification -- Specifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study -- Interesting Fuzzy Association Rules in Quantitative 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 -- Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB -- Using Grammatical Inference to Automate Information Extraction from the Web -- Biological Sequence Data Mining -- Implication-Based Fuzzy Association Rules -- A General Measure of Rule Interestingness -- Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization -- Propositionalisation and Aggregates -- Algorithms for the Construction of Concept Lattices and Their Diagram Graphs -- Data Reduction Using Multiple Models Integration -- Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution -- Sentence Filtering for Information Extraction in Genomics, a Classification Problem -- Text Categorization and Semantic Browsing with Self-Organizing Maps on Non-euclidean Spaces -- A Study on the Hierarchical Data Clustering Algorithm Based on Gravity Theory -- Internet Document Filtering Using Fourier Domain Scoring -- Distinguishing Natural Language Processes on the Basis of fMRI-Measured Brain Activation -- Automatic Construction and Refinement of a Class Hierarchy over Multi-valued Data -- Comparison of Three Objective Functions for Conceptual Clustering -- Identification of ECG Arrhythmias Using Phase Space Reconstruction -- Finding Association Rules That Trade Support Optimally against Confidence -- Bloomy Decision Tree for Multi-objective Classification -- Discovery of Temporal Knowledge in Medical Time-Series Databases Using Moving Average, Multiscale Matching, and Rule Induction -- Mining Positive and Negative Knowledge in Clinical Databases Based on Rough Set Model -- The TwoKey Plot for Multiple Association Rules Control -- Lightweight Collaborative Filtering Method for Binary-Encoded Data -- Invited Papers -- Support Vectors for Reinforcement Learning -- Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining -- Statistification or Mystification? The Need for Statistical Thought in VisualData Mining -- The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery -- Scalability, Search, and Sampling: From Smart Algorithms to Active 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.