数据开采与知识发现原理/会议录Principles of data mining and knowledge discovery

出版时间：2002-09-17  出版社：Springer  作者：Tapio Elomaa  页数：514  

内容概要

This book constitutes the refereed proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2002, held in Helsinki, Finland in August 2002.The 39 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are kernel methods, probabilistic methods, association rule mining, rough sets, sampling algorithms, pattern discovery, web text mining, meta data clustering, rule induction, information extraction, dependency detection, rare class prediction, classifier systems, text classification, temporal sequence analysis, unsupervised learning, time series analysis, medical data mining, etc.

书籍目录

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 　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 InductionAlgebraic Techniques for Analysis of Large Discrete-Valued Datasets　Geography of Differences between Two Classes of Data……Invited PapersAuthor Index