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

Front Cover
Springer Science & Business Media, Apr 5, 2000 - Computers - 460 pages
1 Review
The Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000) was held at the Keihanna-Plaza, Kyoto, Japan, April 18 - 20, 2000. PAKDD 2000 provided an international forum for researchers and applica tion developers to share their original research results and practical development experiences. A wide range of current KDD topics were covered including ma chine learning, databases, statistics, knowledge acquisition, data visualization, knowledge-based systems, soft computing, and high performance computing. It followed the success of PAKDD 97 in Singapore, PAKDD 98 in Austraha, and PAKDD 99 in China by bringing together participants from universities, indus try, and government from all over the world to exchange problems and challenges and to disseminate the recently developed KDD techniques. This PAKDD 2000 proceedings volume addresses both current issues and novel approaches in regards to theory, methodology, and real world application. The technical sessions were organized according to subtopics such as Data Mining Theory, Feature Selection and Transformation, Clustering, Application of Data Mining, Association Rules, Induction, Text Mining, Web and Graph Mining. Of the 116 worldwide submissions, 33 regular papers and 16 short papers were accepted for presentation at the conference and included in this volume. Each submission was critically reviewed by two to four program committee members based on their relevance, originality, quality, and clarity.
 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Perspective on Data Mining from Statistical Viewpoints
1
Inductive Databases and Knowledge Scouts
2
HyperlinkAware Mining and Analysis of the Web
4
Polynomial Time Matching Algorithms for TreeLike Structured Patterns in Knowledge Discovery
5
Fast Discovery of Interesting Rules
17
Performance Controlled Data Reduction for Knowledge Discovery in Distributed Databases
29
Minimum Message Length Criterion for SecondOrder Polynomial Model Discovery
40
Frequent Itemset Counting Across Multiple Tables
49
Mining Web Transaction Patterns in an Electronic Commerce Environment
216
Making Use of the Most Expressive Jumping Emerging Patterns for Classification
220
Mining Structured Association Patterns from Databases
233
Association Rules
245
DensityBased Mining of Quantitative Association Rules
257
A Visualization System for Discovering Numeric Association Rules
269
Discovering Unordered and Ordered Phrase Association Patterns for Text Mining
281
Using Random Walks for Mining Web Document Associations
294

Frequent Closures as a Concise Representation for Binary Data Mining
62
An Optimization Problem in Data Cube System Design
74
Exception Rule Mining with a Relative Interestingness Measure
86
Consistency Based Feature Selection
98
Feature Selection for Clustering
110
A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases
122
Missing Value Estimation Based on Dynamic Attribute Selection
134
On Association Similarity and Dependency of Attributes
138
Prototype Generation Based on Instance Filtering and Averaging
142
A Visual Method of Cluster Validation with Fastmap
153
Clustering with Obstacles Entities A Preliminary Study
165
Combining Sampling Technique with DBSCAN Algorithm for Clustering Large Spatial Databases1
169
Predictive Adaptive Resonance Theory and Knowledge Discovery in Databases
173
Improving Generalization Ability of SelfGenerating Neural Networks Through Ensemble Averaging
177
Attribute Transformations on Numerical Databases
181
Efficient Detection of Local Interactions in the Cascade Model
193
Empirical Study on Data Mining Methods
204
Evaluating HypothesisDriven ExceptionRule Discovery with Medical Data Sets
208
Discovering Protein Functional Models Using Inductive Logic Programming
212
A Concurrent Approach to the KeyPreserving AttributeOriented Induction Method
306
Scaling Up a BoostingBased Learner via Adaptive Sampling
317
Adaptive Boosting for Spatial Functions with Unstable Driving Attributes
329
Robust Ensemble Learning for Data Mining
341
Interactive Visualization in Mining Large Decision Trees
345
Vector Quantization for Decision Tree Induction
349
Making Knowledge Extraction and Reasoning Closer
360
Discovery of Relevant Weights by Minimizing CrossValidation Error
372
Efficient and Comprehensible Local Regression
376
Information Granules for Spatial Reasoning
380
Uncovering the Hierarchical Structure of Text Archives by Using an Unsupervised Neural Network with Adaptive Architecture
384
Mining Access Patterns Efficiently from Web Logs
396
A Comparative Study of Classification Based Personal Email Filtering
408
Extension of GraphBased Induction for General Graph Structured Data
420
TextSource Discovery and GIOSS Update in a Dynamic Web
432
Extraction of Fuzzy Clusters from Weighted Graphs
442
Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms
454
Author Index
458
Copyright

Other editions - View all

Common terms and phrases

About the author (2000)

Terano, University of Tsukuba, Tokyo, Japan.

Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in Computer Science and Electrical Engineering at Shanghai JiaoTong University. Before he joined ASU, he worked at Telecom Australia Research Labs and was on the faculty at National University of Singapore. He was recognized for excellence in teaching and research in Computer Science and Engineering at Arizona State University. His research interests are in data mining, machine learning, social computing, feature selection, and artificial intelligence, investigating problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He is Editor in Chief of ACM Transaction on Intelligent Systems and Technology (TIST), serves on journal editorial boards and numerous conference program committees, and is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction. He is an IEEE Fellow.

Chen, National Tsing Hua University, Hsinchu, Taiwan.