Principles of Data Mining and Knowledge Discovery : Third European Conference, PKDD'99 Prague, Czech Republic, September 15-18, 1999 Proceedings /
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| Author / Creator: | Zytkow, Jan. |
|---|---|
| Imprint: | Berlin, Heidelberg : Springer Berlin Heidelberg, 1999. |
| Language: | English |
| Series: | Lecture notes in computer science, 0302-9743 ; 1704 |
| Subject: | |
| Format: | E-Resource |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/7355948 |
Table of Contents:
- Session 1A. - Time Series
- Scaling up Dynamic Time Warping to Massive Dataset
- The Haar Wavelet Transform in the Time Series Similarity Paradigm
- Rule Discovery in Large Time-Series Medical Databases
- Session 1B. - Applications
- Simultaneous Prediction of Multiple Chemical Parameters of River Water Quality with TILDE
- Applying Data Mining Techniques to Wafer Manufacturing
- An Application of Data Mining to the Problem of the University Students' Dropout Using Markov Chains
- Session 2A. - Taxonomies and Partitions
- Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD
- Taxonomy Formation by Approximate Equivalence Relations, Revisited
- On the Use of Self-Organizing Maps for Clustering and Visualization
- Speeding Up the Search for Optimal Partitions
- Session 2B. - Logic Methods
- Experiments in Meta-level Learning with ILP
- Boolean Reasoning Scheme with Some Applications in Data Mining
- On the Correspondence between Classes of Implicational and Equivalence Quantifiers
- Querying Inductive Databases via Logic-Based User-Defined Aggregates
- Session 3A. - Distributed and Multirelational Databases
- Peculiarity Oriented Multi-database Mining
- Knowledge Discovery in Medical Multi-databases: A Rough Set Approach
- Automated Discovery of Rules and Exceptions from Distributed Databases Using Aggregates
- Session 3B. - Text Mining and Feature Selection
- Text Mining via Information Extraction
- TopCat: Data Mining for Topic Identification in a Text Corpus
- Selection and Statistical Validation of Features and Prototypes
- Session 4A. - Rules and Induction
- Taming Large Rule Models in Rough Set Approaches
- Optimizing Disjunctive Association Rules
- Contribution of Boosting in Wrapper Models
- Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme
- Session 5A. - Interesting and Unusual
- Heuristic Measures of Interestingness
- Enhancing Rule Interestingness for Neuro-fuzzy Systems
- Unsupervised Profiling for Identifying Superimposed Fraud
- OPTICS-OF: Identifying Local Outliers
- Posters
- Selective Propositionalization for Relational Learning
- Circle Graphs: New Visualization Tools for Text-Mining
- On the Consistency of Information Filters for Lazy Learning Algorithms
- Using Genetic Algorithms to Evolve a Rule Hierarchy
- Mining Temporal Features in Association Rules
- The Improvement of Response Modeling: Combining Rule-Induction and Case-Based Reasoning
- Analyzing an Email Collection Using Formal Concept Analysis
- Business Focused Evaluation Methods: A Case Study
- Combining Data and Knowledge by MaxEnt-Optimization of Probability Distributions
- Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation?
- Rough Dependencies as a Particular Case of Correlation: Application to the Calculation of Approximative Reducts
- A Fuzzy Beam-Search Rule Induction Algorithm
- An Innovative GA-Based Decision Tree Classifier in Large Scale Data Mining
- Extension to C-means Algorithm for the Use of Similarity Functions
- Predicting Chemical Carcinogenesis Using Structural Information Only
- LA - A Clustering Algorithm with an Automated Selection of Attributes, which Is Invariant to Functional Transformations of Coordinates
- Association Rule Selection in a Data Mining Environment
- Multi-relational Decision Tree Induction
- Learning of Simple Conceptual Graphs from Positive and Negative Examples
- An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction
- ZigZag, a New Clustering Algorithm to Analyze Categorical Variable Cross-Classification Tables
- Efficient Mining of High Confidence Association Rules without Support Thresholds
- A Logical Approach to Fuzzy Data Analysis
- AST: Support for Algorithm Selection with a CBR Approach
- Efficient Shared Near Neighbours Clustering of Large Metric Data Sets
- Discovery of "Interesting" Data Dependencies from a Workload of SQL Statements
- Learning from Highly Structured Data by Decomposition
- Combinatorial Approach for Data Binarization
- Extending Attribute-Oriented Induction as a Key-Preserving Data Mining Method
- Automated Discovery of Polynomials by Inductive Genetic Programming
- Diagnosing Acute Appendicitis with Very Simple Classification Rules
- Rule Induction in Cascade Model Based on Sum of Squares Decomposition
- Maintenance of Discovered Knowledge
- A Divisive Initialization Method for Clustering Algorithms
- A Comparison of Model Selection Procedures for Predicting Turning Points in Financial Time Series
- Mining Lemma Disambiguation Rules from Czech Corpora
- Adding Temporal Semantics to Association Rules
- Studying the Behavior of Generalized Entropy in Induction Trees Using a M-of-N Concept
- Discovering Rules in Information Trees
- Mining Text Archives: Creating Readable Maps to Structure and Describe Document Collections
- Neuro-fuzzy Data Mining for Target Group Selection in Retail Banking
- Mining Possibilistic Set-Valued Rules by Generating Prime Disjunctions
- Towards Discovery of Information Granules
- Classification Algorithms Based on Linear Combinations of Features
- Managing Interesting Rules in Sequence Mining
- Support Vector Machines for Knowledge Discovery
- Regression by Feature Projections
- Generating Linguistic Fuzzy Rules for Pattern Classification with Genetic Algorithms
- Tutorials
- Data Mining for Robust Business Intelligence Solutions
- Query Languages for Knowledge Discovery in Databases
- The ESPRIT Project CreditMine and Its Relevance for the Internet Market
- Logics and Statistics for Association Rules and Beyond
- Data Mining for the Web
- Relational Learning and Inductive Logic Programming Made Easy
- Author Index