Principles of Data Mining and Knowledge Discovery : 6th European Conference, PKDD 2002, Helsinki, Finland, August 19-23, 2002, Proceedings / edited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen.

Format:

Book

Contributor:

Elomaa, Tapio, 1963- editor.

Mannila, Heikki, editor.

Toivonen, Hannu, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 2431.

Lecture Notes in Artificial Intelligence ; 2431

Language:

English

Subjects (All):

Database management.

Artificial intelligence.

Logic, Symbolic and mathematical.

Mathematical statistics.

Natural language processing (Computer science).

Information storage and retrieval.

Database Management.

Artificial Intelligence.

Mathematical Logic and Formal Languages.

Probability and Statistics in Computer Science.

Natural Language Processing (NLP).

Information Storage and Retrieval.

Local Subjects:

Database Management.

Artificial Intelligence.

Mathematical Logic and Formal Languages.

Probability and Statistics in Computer Science.

Natural Language Processing (NLP).

Information Storage and Retrieval.

Physical Description:

1 online resource (XIV, 514 pages).

Edition:

First edition 2002.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2002.

System Details:

text file PDF

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-540-45681-0

9783540456810

Access Restriction:

Restricted for use by site license.