知识发现和数据开采进展

　　This book constitutes the refereed proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining， PAKDD 2001， held in Hong Kong， China in April 2001.The 38 revised full papers and 22 short papers presented were carefully reviewed and selected from a total of 152 submissions. The book offers topical sections on Web mining， text mining， applications and tools， concept hierarchies， feature selection， interestingness， sequence mining， spatial and temporal mining， association mining， classification and rule induction， clustering， and advanced topics and new methods.

暂缺《知识发现和数据开采进展》作者简介

Keynote Presentations
　Incompleteness in Data Mining
　Mining E-Commerce Data: The Good, the Bad, and the Ugly
　Seamless Integration of Data Mining with DBMS and Applications
Web Mining
　Applying Pattern Mining to Web Information Extraction
　Empirical Study of Recommender Systems Using Linear Classifiers
　i JADE eMiner--A Web-Based Mining Agent Based on Intelligent Java　Agent Development Environment (i JADE) on Internet Shopping
　A Characterized Rating Recommend System
　Discovery of Frequent Tree Structured Patterns in Semistructured Web　Documents
Text Mining
　Text Categorization Using Weight Adjusted k-Nearest　Neighbor Classification
　Predictive Self-Organizing Networks for Text Categorization
　Meta-learning Models for Automatic Textual Document Categorization
　Efficient Algorithms for Concept Space Construction
Topic Detection，Neural NetworksTracking, and Trend Analysis Using Self-Organizing
　Automatic Hypertext Construction through a Text Mining Approach by　Self-Organizing Maps
Applications and Tools
　Semantic Expectation-Based Causation Knowledge Extraction: A Study　on Hong Kong Stock Movement Analysis
　A Toolbox Approach to Flexible and Efficient Data Mining
　Determining Progression in Glaucoma Using Visual Fields
　Seabreeze Prediction Using Bayesian Networks
　Semi-supervised Learning in Medical Image Database
　On Application of Rough Data Mining Methods to Automatic　Construction of Student Models
Concept Hierarchies
　Concept Approximation in Concept Lattice
　Generating Concept Hierarchies/Networks: Mining Additional Semantics　in Relational Data
　Representing Large Concept Hierarchies Using Lattice Data Structure
Feature Selection
　Feature Selection for Temporal Health Records
……
Interestingness
Sequence4 Mining
Spatial and Temporal Mining
Association Mining
Classification and Rule Induction
Clustering
Advanced Topics and New Methods
Author Index