## Principles of Data Mining and Knowledge Discovery: 5th European Conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001 Proceedings, Volume 5 (Google eBook)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. |

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### Contents

1 | |

Automatic Text Summarization Using Unsupervised and Semisupervised Learning | 16 |

An Application to Demographic Data | 29 |

Knowledge Discovery in Multilabel Phenotype Data | 42 |

Computing Association Rules Using Partial Totals | 54 |

A Genetic Algorithms Approach to Cladistics | 67 |

Parametric Approximation Algorithms for HighDimensional Euclidean Similarity | 79 |

Data Structures for Minimization of Total WithinGroup Distance for Spatiotemporal Clustering | 91 |

Data Reduction Using Multiple Models Integration | 301 |

Discovering Fuzzy Classification Rules with Genetic Programming and Coevolution | 314 |

Sentence Filtering for Information Extraction in Genomics a Classification Problem | 326 |

Text Categorization and Semantic Browsing with SelfOrganizing Maps on Noneuclidean Spaces | 338 |

A Study on the Hierarchical Data Clustering Algorithm Based on Gravity Theory | 350 |

Internet Document Filtering Using Fourier Domain Scoring | 362 |

Distinguishing Natural Language Processes on the Basis of fMRIMeasured Brain Activation | 374 |

Automatic Construction and Refinement of a Class Hierarchy over Multivalued Data | 386 |

Noncrisp Clustering by Fast Convergent and Robust Algorithms | 103 |

Pattern Extraction for Time Series Classification | 115 |

A Case Study | 128 |

Interesting Fuzzy Association Rules in Quantitative Databases | 140 |

Interestingness Measures for Fuzzy Association Rules | 152 |

A Data Set Oriented Approach for Clustering Algorithm Selection | 165 |

Fusion of Metaknowledge and Metadata for CaseBased Model Selection | 180 |

Learning Rules about the Qualitative Behaviour of Time Series | 192 |

Temporal Rule Discovery for TimeSeries Satellite Images and Integration with RDB | 204 |

Using Grammatical Inference to Automate Information Extraction from the Web | 216 |

Biological Sequence Data Mining | 228 |

ImplicationBased Fuzzy Association Rules | 241 |

A General Measure of Rule Interestingness | 253 |

Error Correcting Codes with Optimized KullbackLeibler Distances for Text Categorization | 266 |

Propositionalisation and Aggregates | 277 |

Algorithms for the Construction of Concept Lattices and Their Diagram Graphs | 289 |

Comparison of Three Objective Functions for Conceptual Clustering | 399 |

Identification of ECG Arrhythmias Using Phase Space Reconstruction | 411 |

Finding Association Rules That Trade Support Optimally against Confidence | 424 |

Bloomy Decision Tree for Multiobjective Classification | 436 |

Discovery of Temporal Knowledge in Medical TimeSeries Databases Using Moving Average Multiscale Matching and Rule Induction | 448 |

Mining Positive and Negative Knowledge in Clinical Databases Based on Rough Set Model | 460 |

The TwoKey Plot for Multiple Association Rules Control | 472 |

Lightweight Collaborative Filtering Method for BinaryEncoded Data | 484 |

Support Vectors for Reinforcement Learning | 492 |

Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining | 493 |

Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining | 494 |

A Challenge for Machine Learning and Knowledge Discovery | 495 |

From Smart Algorithms to Active Discovery | 507 |

509 | |

### Common terms and phrases

aggregates analysis applied approach Apriori Apriori algorithm association rules attributes average Berlin Heidelberg 2001 candidate cladistics class dimension classification clustering algorithm collaborative filtering complexity computed consider construction corresponding data clustering Data Mining data set database decision tree defined denote described distance distribution documents domain evaluation example experimental experiments extraction frequent function fuzzy association rules fuzzy sets gene graph gravity-based Hidden Markov Model implementation induction input interval itemsets iteration Knowledge Discovery lattice learning algorithm LNAI Machine Learning method motif mutation node optimization P-tree paper parameters partitions patterns performance phase PKDD problem Proc Proceedings proposed pruning quadtree query Raedt records relevant S_Dbw Sect selection self-similar sentences sequence shows Siebes Eds similar space spatial SSLHMM structure subset subtree supervised learning support vector machines Table techniques temporal threshold tion values variables vector voxels