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

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Elomaa, Tapio (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Mannila, Heikki (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Toivonen, Hannu (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2002.
Έκδοση:1st ed. 2002.
Σειρά:Lecture Notes in Artificial Intelligence ; 2431
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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
  • SVM Classification 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.