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Herausgeber: 
  • Heikki Mannila
  • Tapio Elomaa
  • Hannu Toivonen
  • Principles of Data Mining and Knowledge Discovery: 6th European Conference, PKDD 2002, Helsinki, Finland, August 19–23, 2002, Proceedings 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  August 2002  
    Genre:  EDV / Informatik 
     
    Artificial Intelligence / C / computer science / Data Warehousing / Database Management / database programming / Formal Languages and Automata Theory / Information Retrieval
    ISBN:  9783540440376 
    EAN-Code: 
    9783540440376 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  Lecture Notes in Artificial Intelligence
    #2431 - Lecture Notes in Computer Science  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  825 gr 
    Seiten:  514 
    Illustration:  XIV, 514 p. 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.

      



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