Principles of Data Mining and Knowledge Discovery 6th European Conference, PKDD 2002, Helsinki, Finland, August 19–23, 2002, Proceedings
| Other Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2002, 2002
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| Edition: | 1st ed. 2002 |
| Series: | Lecture Notes in Artificial Intelligence
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- Clustering Transactional Data
- Multiscale Comparison of Temporal Patterns in Time-Series Medical Databases
- Association Rules for Expressing Gradual Dependencies
- Support Approximations Using Bonferroni-Type Inequalities
- Using Condensed Representations for Interactive Association Rule Mining
- Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting
- Dependency Detection in MobiMine and Random Matrices
- Long-Term Learning for Web Search Engines
- Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database
- Involving Aggregate Functions in Multi-relational Search
- Information Extraction in Structured Documents Using Tree Automata Induction
- Algebraic Techniques for Analysis of Large Discrete-Valued Datasets
- Geography of Di.erences between Two Classes of Data
- Rule Induction for Classification of Gene Expression Array Data
- Clustering Ontology-Based Metadata in the Semantic Web
- Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases
- SVMClassification Using Sequences of Phonemes and Syllables
- A Novel Web Text Mining Method Using the Discrete Cosine Transform
- A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases
- Answering the Most Correlated N Association Rules Efficiently
- Mining Hierarchical Decision Rules from Clinical Databases Using Rough Sets and Medical Diagnostic Model
- Efficiently Mining Approximate Models of Associations in Evolving Databases
- Explaining Predictions from a Neural Network Ensemble One at a Time
- Structuring Domain-Specific Text Archives by Deriving a Probabilistic XML DTD
- Separability Index in Supervised Learning
- Invited Papers
- Finding Hidden Factors Using Independent Component Analysis
- Reasoning with Classifiers*
- A Kernel Approach for Learning from Almost Orthogonal Patterns
- Learning with Mixture Models: Concepts and Applications
- Contributed Papers
- Optimized Substructure Discovery for Semi-structured Data
- Fast Outlier Detection in High Dimensional Spaces
- Data Mining in Schizophrenia Research — Preliminary Analysis
- Fast Algorithms for Mining Emerging Patterns
- On the Discovery of Weak Periodicities in Large Time Series
- The Need for Low Bias Algorithms in Classification Learning from Large Data Sets
- Mining All Non-derivable Frequent Itemsets
- Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance
- Finding Association Rules with Some Very Frequent Attributes
- Unsupervised Learning: Self-aggregation in Scaled Principal Component Space*
- A Classification Approach for Prediction of Target Events in Temporal Sequences
- Privacy-Oriented Data Mining by Proof Checking
- Choose Your Words Carefully: An Empirical Study of Feature Selection Metrics for Text Classification
- Generating Actionable Knowledge by Expert-Guided Subgroup Discovery