| Keynote Presentations | |
| Incompleteness in Data Mining | p. 1 |
| Mining E-Commerce Data: The Good, the Bad, and the Ugly | p. 2 |
| Seamless Integration of Data Mining with DBMS and Applications | p. 3 |
| Web Mining | |
| Applying Pattern Mining to Web Information Extraction | p. 4 |
| Empirical Study of Recommender Systems Using Linear Classifiers | p. 16 |
| iJade eMiner-A Web-Based Mining Agent Based on Intelligent Java Agent Development Environment (iJade) on Internet Shopping | p. 28 |
| A Characterized Rating Recommend System | p. 41 |
| Discovery of Frequent Tree Structured Patterns in Semistructured Web Documents | p. 47 |
| Text Mining | |
| Text Categorization Using Weight Adjusted k -Nearest Neighbor Classification | p. 53 |
| Predictive Self-Organizing Networks for Text Categorization | p. 66 |
| Meta-learning Models for Automatic Textual Document Categorization | p. 78 |
| Efficient Algorithms for Concept Space Construction | p. 90 |
| Topic Detection, Tracking, and Trend Analysis Using Self-Organizing Neural Networks | p. 102 |
| Automatic Hypertext Construction through a Text Mining Approach by Self-Organizing Maps | p. 108 |
| Applications and Tools | |
| Semantic Expectation-Based Causation Knowledge Extraction: A Study on Hong Kong Stock Movement Analysis | p. 114 |
| A Toolbox Approach to Flexible and Efficient Data Mining | p. 124 |
| Determining Progression in Glaucoma Using Visual Fields | p. 136 |
| Seabreeze Prediction Using Bayesian Networks | p. 148 |
| Semi-supervised Learning in Medical Image Database | p. 154 |
| On Application of Rough Data Mining Methods to Automatic Construction of Student Models | p. 161 |
| Concept Hierarchies | |
| Concept Approximation in Concept Lattice | p. 167 |
| Generating Concept Hierarchies/Networks: Mining Additional Semantics in Relational Data | p. 174 |
| Representing Large Concept Hierarchies Using Lattice Data Structure | p. 186 |
| Feature Selection | |
| Feature Selection for Temporal Health Records | p. 198 |
| Boosting the Performance of Nearest Neighbour Methods with Feature Selection | p. 210 |
| Feature Selection for Meta-learning | p. 222 |
| Interestingness | |
| Efficient Mining of Niches and Set Routines | p. 234 |
| Evaluation of Interestingness Measures for Ranking Discovered Knowledge | p. 247 |
| Peculiarity Oriented Mining and Its Application for Knowledge Discovery in Amino-Acid Data | p. 260 |
| Sequence Mining | |
| Mining Sequence Patterns from Wind Tunnel Experimental Data for Flight Control | p. 270 |
| Scalable Hierarchical Clustering Method for Sequences of Categorical Values | p. 282 |
| FFS-An I/O-Efficient Algorithm for Mining Frequent Sequences | p. 294 |
| Sequential Index Structure for Content-Based Retrieval | p. 306 |
| Spatial and Temporal Mining | |
| The S2-Tree: An Index Structure for Subsequence Matching of Spatial Objects | p. 312 |
| Temporal Data Mining Using Hidden Markov-Local Polynomial Models | p. 324 |
| Patterns Discovery Based on Time-Series Decomposition | p. 336 |
| Criteria on Proximity Graphs for Boundary Extraction and Spatial Clustering | p. 348 |
| Micro Similarity Queries in Time Series Database | p. 358 |
| Association Mining | |
| Mining Optimal Class Association Rule Set | p. 364 |
| Generating Frequent Patterns with the Frequent Pattern List | p. 376 |
| User-Defined Association Mining | p. 387 |
| Direct and Incremental Computing of Maximal Covering Rules | p. 400 |
| Towards Efficient Data Re-mining (DRM) | p. 406 |
| Data Allocation Algorithm for Parallel Association Rule Discovery | p. 413 |
| Classification and Rule Induction | |
| Direct Domain Knowledge Inclusion in the PA3 Rule Induction Algorithm | p. 421 |
| Hierarchical Classification of Documents with Error Control | p. 433 |
| An Efficient Data Compression Approach to the Classification Task | p. 444 |
| Combining the Strength of Pattern Frequency and Distance for Classification | p. 455 |
| A Scalable Algorithm for Rule Post-pruning of Large Decision Trees | p. 467 |
| Optimizing the Induction of Alternating Decision Trees | p. 477 |
| Building Behaviour Knowledge Space to Make Classification Decision | p. 488 |
| Clustering | |
| Efficient Hierarchical Clustering Algorithms Using Partially Overlapping Partitions | p. 495 |
| A Rough Set-Based Clustering Method with Modification of Equivalence Relations | p. 507 |
| Importance of Individual Variables in the k-Means Algorithm | p. 513 |
| A Hybrid Approach to Clustering in Very Large Databases | p. 519 |
| Advanced Topics and New Methods | |
| A Similarity Indexing Method for the Data Warehousing-Bit-Wise Indexing Method | p. 525 |
| Rule Reduction over Numerical Attributes in Decision Trees Using Multilayer Perceptron | p. 538 |
| Knowledge Acquisition from Both Human Expert and Data | p. 550 |
| Neighborhood Dependencies for Prediction | p. 562 |
| Learning Bayesian Networks with Hidden Variables Using the Combination of EM and Evolutionary Algorithms | p. 568 |
| Interactive Construction of Decision Trees | p. 575 |
| An Improved Learning Algorithm for Augmented Naive Bayes | p. 581 |
| Generalised RBF Networks Trained Using an IBL Algorithm for Mining Symbolic Data | p. 587 |
| Author Index | p. 595 |
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