Principles of Data Mining and Knowledge Discovery [electronic resource] :6th European Conference, PKDD 2002 Helsinki, Finland, August 19–23, 2002 Proceedings /
Contributor(s): Elomaa, Tapio [editor.] | Mannila, Heikki [editor.] | Toivonen, Hannu [editor.] | SpringerLink (Online service).Material type: BookSeries: Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence: 2431Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2002.Description: XIV, 514 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540456810.Subject(s): Computer science | Mathematical logic | Mathematical statistics | Database management | Information storage and retrieval | Artificial intelligence | Text processing (Computer science) | Computer Science | Database Management | Artificial Intelligence (incl. Robotics) | Mathematical Logic and Formal Languages | Probability and Statistics in Computer Science | Document Preparation and Text Processing | Information Storage and RetrievalOnline resources: Click here to access online
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.