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

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.

he LNAI series reports state-of-the-art results in artificial intelligence re-search, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies,LNAI has grown into the most comprehensive artificial intelligence research forum available.
The scope of LNAI spans the whole range of artificial intelligence and intelli-gent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes
　　proceedings (published in time for the respective conference)；
　post-proceedings (consisting of thoroughly revised final full papers)；
　 research monographs (which may be based on Phi) work).

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 Induction
Algebraic Techniques for Analysis of Large Discrete-Valued Datasets
　Geography of Differences between Two Classes of Data
……
Invited Papers
Author Index