Knowledge Discovery and Data Mining. Current Issues and New Applications: Current Issues and New Applications: 4th Pacific-Asia Conference, PAKDD 2000 Kyoto, Japan, April 18-20, 2000 Proceedings / Edition 1by Takao Terano, Huan Liu, Arbee L.P. Chen
Pub. Date: 01/03/2008
Publisher: Springer Berlin Heidelberg
This book constitutes the refereed proceedings of the Fourth Pacific-A sia Conference on Knowledge Discovery and Data Mining, PAKDD 2000, hel d in Kyoto, Japan, in April 2000. The 33 revised full papers and 16 sh ort papers presented were carefully reviewed and selected from a total of 116 submissions. The papers are organized in sections on data mini ng theory;
This book constitutes the refereed proceedings of the Fourth Pacific-A sia Conference on Knowledge Discovery and Data Mining, PAKDD 2000, hel d in Kyoto, Japan, in April 2000. The 33 revised full papers and 16 sh ort papers presented were carefully reviewed and selected from a total of 116 submissions. The papers are organized in sections on data mini ng theory; feature selection and transformation; clustering; applicati ons of data mining; association rules and related topics; induction; t ext, web, and graph mining.
- Springer Berlin Heidelberg
- Publication date:
- Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence Series , #1805
- Edition description:
- Product dimensions:
- 9.21(w) x 6.14(h) x 0.97(d)
Table of Contents
Keynote Speeches and Invited Talk.- Perspective on Data Mining from Statistical Viewpoints.- Inductive Databases and Knowledge Scouts.- Hyperlink-Aware Mining and Analysis of the Web.- Data Mining Theory.- Polynomial Time Matching Algorithms for Tree-Like Structured Patterns in Knowledge Discovery.- Fast Discovery of Interesting Rules.- Performance Controlled Data Reduction for Knowledge Discovery in Distributed Databases.- Minimum Message Length Criterion for Second-Order Polynomial Model Discovery.- Frequent Itemset Counting Across Multiple Tables.- Frequent Closures as a Concise Representation for Binary Data Mining.- An Optimization Problem in Data Cube System Design.- Exception Rule Mining with a Relative Interestingness Measure.- Feature Selection and Transformation.- Consistency Based Feature Selection.- Feature Selection for Clustering.- A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases.- Missing Value Estimation Based on Dynamic Attribute Selection.- On Association, Similarity and Dependency of Attributes.- Clustering.- Prototype Generation Based on Instance Filtering and Averaging.- A Visual Method of Cluster Validation with Fastmap.- COE: Clustering with Obstacles Entities A Preliminary Study.- Combining Sampling Technique with DBSCAN Algorithm for Clustering Large Spatial Databases.- Predictive Adaptive Resonance Theory and Knowledge Discovery in Databases.- Improving Generalization Ability of Self-Generating Neural Networks Through Ensemble Averaging.- Application of Data Mining.- Attribute Transformations on Numerical Databases.- Efficient Detection of Local Interactions in the Cascade Model.- Extracting Predictors of Corporate Bankruptcy: Empirical Study on Data Mining Methods.- Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets.- Discovering Protein Functional Models Using Inductive Logic Programming.- Mining Web Transaction Patterns in an Electronic Commerce Environment.- Association Rules and Related Topics.- Making Use of the Most Expressive Jumping Emerging Patterns for Classification.- Mining Structured Association Patterns from Databases.- Association Rules.- Density-Based Mining of Quantitative Association Rules.- AViz: A Visualization System for Discovering Numeric Association Rules.- Discovering Unordered and Ordered Phrase Association Patterns for Text Mining.- Using Random Walks for Mining Web Document Associations.- Induction.- A Concurrent Approach to the Key-Preserving Attribute-Oriented Induction Method.- Scaling Up a Boosting-Based Learner via Adaptive Sampling.- Adaptive Boosting for Spatial Functions with Unstable Driving Attributes.- Robust Ensemble Learning for Data Mining.- Interactive Visualization in Mining Large Decision Trees.- VQTree: Vector Quantization for Decision Tree Induction.- Making Knowledge Extraction and Reasoning Closer.- Discovery of Relevant Weights by Minimizing Cross-Validation Error.- Efficient and Comprehensible Local Regression.- Information Granules for Spatial Reasoning.- Text, Web, and Graph Mining.- Uncovering the Hierarchical Structure of Text Archives by Using an Unsupervised Neural Network with Adaptive Architecture.- Mining Access Patterns Efficiently from Web Logs.- A Comparative Study of Classification Based Personal E-mail Filtering.- Extension of Graph-Based Induction for General Graph Structured Data.- Text-Source Discovery and GlOSS Update in a Dynamic Web.- Extraction of Fuzzy Clusters from Weighted Graphs.- Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms.
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