Principles of Data Mining and Knowledge Discovery : Third European Conference, PKDD'99, Prague, Czech Republic, September 15-18, 1999, Proceedings

by Pkdd 9 (1999 Prague, Czech Republic); Rauch, Jan; Carbonell, J. G.; Siekmann, J.; Zytkow, Jan M.

• ISBN13: 9783540664901
• ISBN10: 3540664904
• Format: Paperback
• Copyright: 1999-10-01
• Publisher: Springer-Verlag New York Inc

Summary

This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999.The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.

Table of Contents

- Time Series
Scaling up Dynamic Time Warping to Massive Dataset	p. 1
The Haar Wavelet Transform in the Time Series Similarity Paradigm	p. 12
Rule Discovery in Large Time-Series Medical Databases	p. 23
- Applications
Simultaneous Prediction of Multiple Chemical Parameters of River Water Quality with TILDE	p. 32
Applying Data Mining Techniques to Wafer Manufacturing	p. 41
An Application of Data Mining to the Problem of the University Students' Dropout Using Markov Chains	p. 51
- Taxonomies and Partitions
Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD	p. 61
Taxonomy Formation by Approximate Equivalence Relations, Revisited	p. 71
On the Use of Self-Organizing Maps for Clustering and Visualization	p. 80
Speeding Up the Search for Optimal Partitions	p. 89
- Logic Methods
Experiments in Meta-level Learning with ILP	p. 98
Boolean Reasoning Scheme with Some Applications in Data Mining	p. 107
On the Correspondence between Classes of Implicational and Equivalence Quantifiers	p. 116
Querying Inductive Databases via Logic-Based User-Defined Aggregates	p. 125
- Distributed and Multirelational Databases
Peculiarity Oriented Multi-database Mining	p. 136
Knowledge Discovery in Medical Multi-databases: A Rough Set Approach	p. 147
Automated Discovery of Rules and Exceptions from Distributed Databases Using Aggregates	p. 156
- Text Mining and Feature Selection
Text Mining via Information Extraction	p. 165
TopCat: Data Mining for Topic Identification in a Text Corpus	p. 174
Selection and Statistical Validation of Features and Prototypes	p. 184
- Rules and Induction
Taming Large Rule Models in Rough Set Approaches	p. 193
Optimizing Disjunctive Association Rules	p. 204
Contribution of Boosting in Wrapper Models	p. 214
Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme	p. 223
- Interesting and Unusual
Heuristic Measures of Interestingness	p. 232
Enhancing Rule Interestingness for Neuro-fuzzy Systems	p. 242
Unsupervised Profiling for Identifying Superimposed Fraud	p. 251
OPTICS-OF: Identifying Local Outliers	p. 262
Posters
Selective Propositionalization for Relational Learning	p. 271
Circle Graphs: New Visualization Tools for Text-Mining	p. 277
On the Consistency of Information Filters for Lazy Learning Algorithms	p. 283
Using Genetic Algorithms to Evolve a Rule Hierarchy	p. 289
Mining Temporal Features in Association Rules	p. 295
The Improvement of Response Modeling: Combining Rule-Induction and Case-Based Reasoning	p. 301
Analyzing an Email Collection Using Formal Concept Analysis	p. 309
Business Focused Evaluation Methods: A Case Study	p. 316
Combining Data and Knowledge by MaxEnt-Optimization of Probability Distributions	p. 323
Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation?	p. 329
Rough Dependencies as a Particular Case of Correlation: Application to the Calculation of Approximative Reducts	p. 335
A Fuzzy Beam-Search Rule Induction Algorithm	p. 341
An Innovative GA-Based Decision Tree Classifier in Large Scale Data Mining	p. 348
Extension to C-means Algorithm for the Use of Similarity Functions	p. 354
Predicting Chemical Carcinogenesis Using Structural Information Only	p. 360
LA - A Clustering Algorithm with an Automated Selection of Attributes, which Is Invariant to Functional Transformations of Coordinates	p. 366
Association Rule Selection in a Data Mining Environment	p. 372
Multi-relational Decision Tree Induction	p. 378
Learning of Simple Conceptual Graphs from Positive and Negative Examples	p. 384
An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction	p. 392
ZigZag, a New Clustering Algorithm to Analyze Categorical Variable Cross-Classification Tables	p. 398
Efficient Mining of High Confidence Association Rules without Support Thresholds	p. 406
A Logical Approach to Fuzzy Data Analysis	p. 412
AST: Support for Algorithm Selection with a CBR Approach	p. 418
Efficient Shared Near Neighbours Clustering of Large Metric Data Sets	p. 424
Discovery of "Interesting" Data Dependencies from a Workload of SQL Statements	p. 430
Learning from Highly Structured Data by Decomposition	p. 436
Combinatorial Approach for Data Binarization	p. 442
Extending Attribute-Oriented Induction as a Key-Preserving Data Mining Method	p. 448
Automated Discovery of Polynomials by Inductive Genetic Programming	p. 456
Diagnosing Acute Appendicitis with Very Simple Classification Rules	p. 462
Rule Induction in Cascade Model Based on Sum of Squares Decomposition	p. 468
Maintenance of Discovered Knowledge	p. 476
A Divisive Initialization Method for Clustering Algorithms	p. 484
A Comparison of Model Selection Procedures for Predicting Turning Points in Financial Time Series	p. 492
Mining Lemma Disambiguation Rules from Czech Corpora	p. 498
Adding Temporal Semantics to Association Rules	p. 504
Studying the Behavior of Generalized Entropy in Induction Trees Using a M-of-N Concept	p. 510
Discovering Rules in Information Trees	p. 518
Mining Text Archives: Creating Readable Maps to Structure and Describe Document Collections	p. 524
Neuro-fuzzy Data Mining for Target Group Selection in Retail Banking	p. 530
Mining Possibilistic Set-Valued Rules by Generating Prime Disjunctions	p. 536
Towards Discovery of Information Granules	p. 542
Classification Algorithms Based on Linear Combinations of Features	p. 548
Managing Interesting Rules in Sequence Mining	p. 554
Support Vector Machines for Knowledge Discovery	p. 561
Regression by Feature Projections	p. 568
Generating Linguistic Fuzzy Rules for Pattern Classification with Genetic Algorithms	p. 574
Tutorials
Data Mining for Robust Business Intelligence Solutions	p. 580
Query Languages for Knowledge Discovery in Databases	p. 582
The ESPRIT Project CreditMine and Its Relevance for the Internet Market	p. 584
Logics and Statistics for Association Rules and Beyond	p. 586
Data Mining for the Web	p. 588
Relational Learning and Inductive Logic Programming Made Easy	p. 590
Author Index	p. 591
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