TY - BOOK
AU - Elomaa,Tapio
AU - Mannila,Heikki
AU - Toivonen,Hannu
ED - SpringerLink (Online service)
TI - Principles of Data Mining and Knowledge Discovery: 6th European Conference, PKDD 2002 Helsinki, Finland, August 19–23, 2002 Proceedings
T2 - Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence,
SN - 9783540456810
AV - QA76.9.D3
U1 - 005.74 23
PY - 2002///
CY - Berlin, Heidelberg
PB - Springer Berlin Heidelberg
KW - Computer science
KW - Mathematical logic
KW - Mathematical statistics
KW - Database management
KW - Information storage and retrieval
KW - Artificial intelligence
KW - Text processing (Computer science)
KW - Computer Science
KW - Database Management
KW - Artificial Intelligence (incl. Robotics)
KW - Mathematical Logic and Formal Languages
KW - Probability and Statistics in Computer Science
KW - Document Preparation and Text Processing
KW - Information Storage and Retrieval
N1 - 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
UR - http://dx.doi.org/10.1007/3-540-45681-3
ER -