Principles of data mining and knowledge discovery : 6th European Conference, PKDD 2002, Helsinki, Finland, August 19 23, 2002 : proceedings
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| Daljnji autori: | , , |
| Format: | Livre numérique |
| Jezik: | Anglais |
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Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serija: | Lecture notes in computer science. Lecture notes in artificial intelligence
2431 |
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| Online pristup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Principles of Data Mining and Knowledge Discovery, 6th European Conference, PKDD 2002, Helsinki, Finland, August 19-23, 2002, Proceedings, Tapio Elomaa, Heikki Mannila, Hannu Toivonen (eds.), Berlin, Springer, 2002, 1 vol. (XIV-514 p.), Lecture notes in computer science, 3-540-44037-2 • Principles of Data Mining and Knowledge Discovery, Texte imprimé, 9783662209820 |
Sadržaj:
- 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
- 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.