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 ProceedingsTakao Terano, Huan Liu, Arbee L.P. Chen Springer Science & Business Media, 2007 M07 13 - 462 pages 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. |
Contents
| 1 | |
Fast Discovery of Interesting Rules | 17 |
Minimum Message Length Criterion for SecondOrder Polynomial Model | 40 |
Frequent Closures as a Concise Representation for Binary Data Mining | 62 |
Exception Rule Mining with a Relative Interestingness Measure | 86 |
Feature Selection for Clustering | 110 |
Missing Value Estimation Based on Dynamic Attribute Selection | 134 |
A Visual Method of Cluster Validation with Fastmap | 153 |
DensityBased Mining of Quantitative Association Rules | 257 |
Discovering Unordered and Ordered Phrase Association Patterns for Text | 281 |
Using Random Walks for Mining Web Document Associations | 294 |
Scaling Up a BoostingBased Learner via Adaptive Sampling | 317 |
Robust Ensemble Learning for Data Mining | 341 |
Making Knowledge Extraction and Reasoning Closer | 360 |
Efficient and Comprehensible Local Regression | 376 |
Mining Access Patterns Efficiently from Web Logs | 396 |
Combining Sampling Technique with DBSCAN Algorithm for Clustering | 169 |
Efficient Detection of Local Interactions in the Cascade Model | 193 |
Evaluating HypothesisDriven ExceptionRule Discovery with Medical | 208 |
Mining Structured Association Patterns from Databases | 233 |
Extension of GraphBased Induction for General Graph Structured Data | 420 |
Extraction of Fuzzy Clusters from Weighted Graphs | 442 |
| 459 | |
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Common terms and phrases
A.L.P. Chen Eds access patterns access sequence AdaBoost Agrawal analysis applied approach Apriori Apriori algorithm Artificial Intelligence association rules attribute values average Berlin Heidelberg 2000 boosting classification accuracy classifier consider data cube data mining data set decision stumps decision tree defined denote dense regions dimensional discovering Discovery and Data distance document E-mail efficient error evaluation example extracted Fastmap feature selection filtering frequent function graph induction input instances interesting interestingness itemsets iteration JEP-Classifier JEPs k-NN Knowledge Discovery learning algorithms LNAI local regression Machine Learning matching measure merge method neural networks node optimal PAKDD performance problem Proc prototype pruning quantization query reduced regular term tree represent retrieval sampling Section self-organizing map space spatial Springer-Verlag Berlin Heidelberg structure subset techniques Terano threshold training set transaction tuples variables vector vertex visualization WAP-tree