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Principles of Data Mining and Knowledge Discovery 6th European Conference, PKDD 2002, Helsinki, Finland, August 19–23, 2002, Proceedings

Aus der Reihe Lecture Notes in Artificial Intelligence

49,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

05.08.2002

Abbildungen

XIV, 514 p.

Herausgeber

Tapio Elomaa + weitere

Verlag

Springer Berlin

Seitenzahl

514

Maße (L/B/H)

23,5/15,5/3 cm

Gewicht

814 g

Auflage

2002

Sprache

Englisch

ISBN

978-3-540-44037-6

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

05.08.2002

Abbildungen

XIV, 514 p.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

514

Maße (L/B/H)

23,5/15,5/3 cm

Gewicht

814 g

Auflage

2002

Sprache

Englisch

ISBN

978-3-540-44037-6

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Principles of Data Mining and Knowledge Discovery
  • Produktbild: Principles of Data Mining and Knowledge Discovery
  • 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.