5. PAKDD 2001: Hong Kong, China

Keynote Presentations

• H. V. Jagadish:
  Incompleteness in Data Mining. 1
  
• Ron Kohavi:
  Mining E-Commerce Data: The Good, the Bad, and the Ugly. 2
  
• Hongjun Lu:
  Seamless Integration of Data Mining with DBMS and Applications. 3
  

Web Mining

• Chia-Hui Chang, Shao-Chen Lui, Yen-Chin Wu:
  Applying Pattern Mining to Web Information Extraction. 4-16
  
• Vijay S. Iyengar, Tong Zhang:
  Empirical Study of Recommender Systems Using Linear Classifiers. 16-27
  
• Raymond S. T. Lee, James N. K. Liu:
  iJADE eMiner - A Web-Based Mining Agent Based on Intelligent Java Agent Development Environment (iJADE) on Internet Shopping. 28-40
  
• Yao-Tsung Lin, Shian-Shyong Tseng:
  A Characterized Rating Recommend System. 41-46
  
• Tetsuhiro Miyahara, Takayoshi Shoudai, Tomoyuki Uchida, Kenichi Takahashi, Hiroaki Ueda:
  Discovery of Frequent Tree Structured Patterns in Semistructured Web Documents. 47-52
  

Text Mining

• Eui-Hong Han, George Karypis, Vipin Kumar:
  Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification. 53-65
  
• Ah-Hwee Tan:
  Predictive Self-Organizing Networks for Text Categorization. 66-77
  
• Kwok-Yin Lai, Wai Lam:
  Meta-learning Models for Automatic Textual Document Categorization. 78-89
  
• Chi-Yuen Ng, Joseph Lee, Felix Cheung, Ben Kao, David Wai-Lok Cheung:
  Efficient Algorithms for Concept Space Construction. 90-101
  
• Kanagasabai Rajaraman, Ah-Hwee Tan:
  Topic Detection, Tracking, and Trend Analysis Using Self-Organizing Neural Networks. 102-107
  
• Hsin-Chang Yang, Chung-Hong Lee:
  Automatic Hypertext Construction through a Text Mining Approach by Self-Organizing Maps. 108-113
  

Applications and Tools

• Boon Toh Low, Ki Chan, Lei-Lei Choi, Man-Yee Chin, Sin-Ling Lay:
  Semantic Expectation-Based Causation Knowledge Extraction: A Study on Hong Kong Stock Movement Analysis. 114-123
  
• Ole Møller Nielsen, Peter Christen, Markus Hegland, Tatiana Semenova, Timothy Hancock:
  A Toolbox Approach to Flexible and Efficient Data Mining. 124-135
  
• Andrew Turpin, Eibe Frank, Mark Hall, Ian H. Witten, Chris A. Johnson:
  Determining Progression in Glaucoma Using Visual Fields. 136-147
  
• Russell J. Kennett, Kevin B. Korb, Ann E. Nicholson:
  Seabreeze Prediction Using Bayesian Networks. 148-153
  
• Chun Hung Li, Pong Chi Yuen:
  Semi-supervised Learning in Medical Image Database. 154-160
  
• Feng-Hsu Wang, Shiou-Wen Hung:
  On Application of Rough Data Mining Methods to Automatic Construction of Student Models. 161-166
  

Concept Hierarchies

• Keyun Hu, Yuefei Sui, Yuchang Lu, Ju Wang, Chunyi Shi:
  Concept Approximation in Concept Lattice. 167-173
  
• Tsau Young Lin:
  Generating Concept Hierarchies/Networks: Mining Additional Semantics in Relational Data. 174-185
  
• Yanee Kachai, Kitsana Waiyamai:
  Representing Large Concept Hierarchies Using Lattice Data Structure. 186-197
  

Feature Selection

• Rohan A. Baxter, Graham J. Williams, Hongxing He:
  Feature Selection for Temporal Health Records. 198-209
  
• Shlomo Geva:
  Boosting the Performance of Nearest Neighbour Methods with Feature Selection. 210-221
  
• Alexandros Kalousis, Melanie Hilario:
  Feature Selection for Meta-learning. 222-233
  

Interestingness

• Guozhu Dong, Kaustubh Deshpande:
  Efficient Mining of Niches and Set Routines. 234-246
  
• Robert J. Hilderman, Howard J. Hamilton:
  Evaluation of Interestingness Measures for Ranking Discovered Knowledge. 247-259
  
• Ning Zhong, Muneaki Ohshima, Setsuo Ohsuga:
  Peculiarity Oriented Mining and Its Application for Knowledge Discovery in Amino-Acid Data. 260-269
  

Sequence Mining

• Zhenyu Liu, Wesley W. Chu, Adam Huang, Chris Folk, Chih-Ming Ho:
  Mining Sequence Patterns from Wind Tunnel Experimental Data for Flight Control. 270-281
  
• Tadeusz Morzy, Marek Wojciechowski, Maciej Zakrzewicz:
  Scalable Hierarchical Clustering Method for Sequences of Categorical Values. 282-293
  
• Minghua Zhang, Ben Kao, Chi Lap Yip, David Wai-Lok Cheung:
  FFS - An I/O-Efficient Algorithm for Mining Frequent Sequences. 294-305
  
• Maciej Zakrzewicz:
  Sequential Index Structure for Content-Based Retrieval. 306-311
  

Spatial and Temporal Mining

• Haixun Wang, Chang-Shing Perng:
  The S²-Tree : An Index Structure for Subsequence Matching of Spatial Objects. 312-323
  
• Weiqiang Lin, Mehmet A. Orgun, Graham J. Williams:
  Temporal Data Mining Using Hidden Markov-Local Polynomial Models. 324-335
  
• Jeffrey Xu Yu, Michael K. Ng, Joshua Zhexue Huang:
  Patterns Discovery Based on Time-Series Decomposition. 336-347
  
• Vladimir Estivill-Castro, Ickjai Lee, Alan T. Murray:
  Criteria on Proximity Graphs for Boundary Extraction and Spatial Clustering. 348-357
  
• Xiaoming Jin, Yuchang Lu, Chunyi Shi:
  Micro Similarity Queries in Time Series Database. 358-363
  

Association Mining

• Jiuyong Li, Hong Shen, Rodney W. Topor:
  Mining Optimal Class Association Rule Set. 364-375
  
• Fan-Chen Tseng, Ching-Chi Hsu:
  Generating Frequent Patterns with the Frequent Pattern List. 376-386
  
• Ke Wang, Yu He:
  User-Defined Association Mining. 387-399
  
• Marzena Kryszkiewicz:
  Direct and Incremental Computing of Maximal Covering Rules. 400-405
  
• Jiming Liu, Jian Yin:
  Towards Efficient Data Re-mining (DRM). 406-412
  
• Anna M. Manning, John A. Keane:
  Data Allocation Algorithm for Parallel Association Rule Discovery. 413-420
  

Classification and Rule Induction

• Pedro de Almeida:
  Direct Domain Knowledge Inclusion in the PA3 Rule Induction Algorithm. 421-432
  
• Chun Hung Cheng, Jian Tang, Ada Wai-Chee Fu, Irwin King:
  Hierarchical Classification of Documents with Error Control. 433-443
  
• Claudia Diamantini, Maurizio Panti:
  An Efficient Data Compression Approach to the Classification Task. 444-454
  
• Jinyan Li, Kotagiri Ramamohanarao, Guozhu Dong:
  Combining the Strength of Pattern Frequency and Distance for Classification. 455-466
  
• Trong Dung Nguyen, Tu Bao Ho, Hiroshi Shimodaira:
  A Scalable Algorithm for Rule Post-pruning of Large Decision Trees. 467-476
  
• Bernhard Pfahringer, Geoffrey Holmes, Richard Kirkby:
  Optimizing the Induction of Alternating Decision Trees. 477-487
  
• Xiuzhen Zhang, Guozhu Dong, Kotagiri Ramamohanarao:
  Building Behaviour Knowledge Space to Make Classification Decision. 488-494
  

Clustering

• Manoranjan Dash, Huan Liu:
  Efficient Hierarchical Clustering Algorithms Using Partially Overlapping Partitions. 495-506
  
• Shoji Hirano, Tomohiro Okuzaki, Yutaka Hata, Shusaku Tsumoto, Kouhei Tsumoto:
  A Rough Set-Based Clustering Method with Modification of Equivalence Relations. 507-512
  
• Juha Vesanto:
  Importance of Individual Variables in the k -Means Algorithm. 513-518
  
• Aoying Zhou, Weining Qian, Hailei Qian, Wen Jin, Shuigeng Zhou, Ye Fan:
  A Hybrid Approach to Clustering in Very Large Databases. 519-524
  

Advanced Topics and New Methods

• Wei-Chou Chen, Shian-Shyong Tseng, Lu-Ping Chang, Mon-Fong Jiang:
  A Similarity Indexing Method for the Data Warehousing - Bit-Wise Indexing Method. 525-537
  
• DaeEun Kim, Jaeho Lee:
  Rule Reduction over Numerical Attributes in Decision Tree Using Multilayer Perceptron. 538-549
  
• Takuya Wada, Hiroshi Motoda, Takashi Washio:
  Knowledge Acquisition from Both Human Expert and Data. 550-561
  
• Renaud Bassée, Jef Wijsen:
  Neighborhood Dependencies for Prediction. 562-567
  
• Fengzhan Tian, Yuchang Lu, Chunyi Shi:
  Learning Bayesian Networks with Hidden Variables Using the Combination of EM and Evolutionary Algorithms. 568-574
  
• Jianchao Han, Nick Cercone:
  Interactive Construction of Decision Trees. 575-580
  
• Huajie Zhang, Charles X. Ling:
  An Improved Learning Algorithm for Augmented Naive Bayes. 581-586
  
• Liviu Vladutu, Stergios Papadimitriou, Seferina Mavroudi, Anastasios Bezerianos:
  Generalised RBF Networks Trained Using an IBL Algorithm for Mining Symbolic Data. 587-594
  

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