Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings (Lecture Notes in Computer Science (3056))

Thepaci?c-asiaconferenceonknowledgediscoveryanddatamining(pakdd) Has Been Held Every Year Since 1997. This Year, The Eighth In The Series (pakdd 2004) Was Held At Carlton Crest Hotel, Sydney, Australia, 26–28 May 2004. Pakdd Is A Leading International Conference In The Area Of Data Mining. It P- Vides An International Forum For Researchers And Industry Practitioners To Share Their New Ideas, Original Research Results And Practical Development Experiences From All Kdd-related Areas Including Data Mining, Data Warehousing, Machine Learning, Databases, Statistics, Knowledge Acquisition And Automatic Scienti?c Discovery, Data Visualization, Causal Induction, And Knowledge-based Systems. The Selection Process This Year Was Extremely Competitive. We Received 238 Researchpapersfrom23countries,whichisthehighestinthehistoryofpakdd, And Re?ects The Recognition Of And Interest In This Conference. Each Submitted Research Paper Was Reviewed By Three Members Of The Program Committee. F- Lowing This Independent Review, There Were Discussions Among The Reviewers, And When Necessary, Additional Reviews From Other Experts Were Requested. A Total Of 50 Papers Were Selected As Full Papers (21%), And Another 31 Were Selected As Short Papers (13%), Yielding A Combined Acceptance Rate Of Approximately 34%. The Conference Accommodated Both Research Papers Presenting Original - Vestigation Results And Industrial Papers Reporting Real Data Mining Applications Andsystemdevelopmentexperience.theconferencealsoincludedthreetutorials On Key Technologies Of Knowledge Discovery And Data Mining, And One Workshop Focusing On Speci?c New Challenges And Emerging Issues Of Knowledge Discovery Anddatamining.thepakdd2004programwasfurtherenhancedwithkeynote Speeches By Two Outstanding Researchers In The Area Of Knowledge Discovery And Data Mining: Philip Yu, Manager Of Software Tools And Techniques, Ibm T.j. Invited Speeches -- Session 1a: Classification (i) -- Session 1b: Clustering (i) -- Session 1c: Association Rules (i) -- Session 2a: Novel Algorithms (i) -- Session 2b: Association (ii) -- Session 2c: Classification (ii) -- Session 3a: Event Mining, Anomaly Detection, And Intrusion Detection -- Session 3b: Ensemble Learning -- Session 3c: Bayesian Network And Graph Mining -- Session 3d: Text Mining (i) -- Session 4a: Clustering (ii) -- Session 4b: Association (iii) -- Session 4c: Novel Algorithms (ii) -- Session 4d: Multimedia Mining -- Session 5a: Text Mining And Web Mining (ii) -- Session 5b: Statistical Methods, Sequential Data Mining, And Time Series Mining -- Session 5c: Novel Algorithms (iii) -- Session 5d: Biomedical Mining. Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang (eds.). Includes Bibliographical References And Index. Also Issued Online. Front Matter....Pages - Mining of Evolving Data Streams with Privacy Preservation....Pages 1-1 Data Mining Grand Challenges....Pages 2-2 Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms....Pages 3-12 Spectral Energy Minimization for Semi-supervised Learning....Pages 13-21 Discriminative Methods for Multi-labeled Classification....Pages 22-30 Subspace Clustering of High Dimensional Spatial Data with Noises....Pages 31-40 Constraint-Based Graph Clustering through Node Sequencing and Partitioning....Pages 41-51 Mining Expressive Process Models by Clustering Workflow Traces....Pages 52-62 CMTreeMiner: Mining Both Closed and Maximal Frequent Subtrees....Pages 63-73 Secure Association Rule Sharing....Pages 74-85 Self-Similar Mining of Time Association Rules....Pages 86-95 ParaDualMiner: An Efficient Parallel Implementation of the DualMiner Algorithm....Pages 96-105 A Novel Distributed Collaborative Filtering Algorithm and Its Implementation on P2P Overlay Network....Pages 106-115 An Efficient Algorithm for Dense Regions Discovery from Large-Scale Data Streams....Pages 116-120 Blind Data Linkage Using n -gram Similarity Comparisons....Pages 121-126 Condensed Representation of Emerging Patterns....Pages 127-132 Discovery of Maximally Frequent Tag Tree Patterns with Contractible Variables from Semistructured Documents....Pages 133-144 Mining Term Association Rules for Heuristic Query Construction....Pages 145-154 FP-Bonsai: The Art of Growing and Pruning Small FP-Trees....Pages 155-160 Mining Negative Rules Using GRD....Pages 161-165 Applying Association Rules for Interesting Recommendations Using Rule Templates....Pages 166-170 Feature Extraction and Classification System for Nonlinear and Online Data....Pages 171-180 A Metric Approach to Building Decision Trees Based on Goodman-Kruskal Association Index....Pages 181-190 DRC-BK: Mining Classification Rules with Help of SVM....Pages 191-195 A New Data Mining Method Using Organizational Coevolutionary Mechanism....Pages 196-200 Noise Tolerant Classification by Chi Emerging Patterns....Pages 201-206 The Application of Emerging Patterns for Improving the Quality of Rare-Class Classification....Pages 207-211 Finding Negative Event-Oriented Patterns in Long Temporal Sequences....Pages 212-221 OBE: Outlier by Example....Pages 222-234 Temporal Sequence Associations for Rare Events....Pages 235-239 Summarization of Spacecraft Telemetry Data by Extracting Significant Temporal Patterns....Pages 240-244 An Extended Negative Selection Algorithm for Anomaly Detection....Pages 245-254 Adaptive Clustering for Network Intrusion Detection....Pages 255-259 Ensembling MML Causal Discovery....Pages 260-271 Logistic Regression and Boosting for Labeled Bags of Instances....Pages 272-281 Fast and Light Boosting for Adaptive Mining of Data Streams....Pages 282-292 Compact Dual Ensembles for Active Learning....Pages 293-297 On the Size of Training Set and the Benefit from Ensemble....Pages 298-307 Identifying Markov Blankets Using Lasso Estimation....Pages 308-318 Selective Augmented Bayesian Network Classifiers Based on Rough Set Theory....Pages 319-328 Using Self-Consistent Naive-Bayes to Detect Masquerades....Pages 329-340 DB-Subdue: Database Approach to Graph Mining....Pages 341-350 Finding Frequent Structural Features among Words in Tree-Structured Documents....Pages 351-360 Exploring Potential of Leave-One-Out Estimator for Calibration of SVM in Text Mining....Pages 361-372 Classifying Text Streams in the Presence of Concept Drifts....Pages 373-383 Using Cluster-Based Sampling to Select Initial Training Set for Active Learning in Text Classification....Pages 384-388 Spectral Analysis of Text Collection for Similarity-Based Clustering....Pages 389-393 Clustering Multi-represented Objects with Noise....Pages 394-403 Providing Diversity in K-Nearest Neighbor Query Results....Pages 404-413 Cluster Structure of K -means Clustering via Principal Component Analysis....Pages 414-418 Combining Clustering with Moving Sequential Pattern Mining: A Novel and Efficient Technique....Pages 419-423 An Alternative Methodology for Mining Seasonal Pattern Using Self-Organizing Map....Pages 424-430 ISM: Item Selection for Marketing with Cross-Selling Considerations....Pages 431-440 Efficient Pattern-Growth Methods for Frequent Tree Pattern Mining....Pages 441-451 Mining Association Rules from Structural Deltas of Historical XML Documents....Pages 452-457 Data Mining Proxy: Serving Large Number of Users for Efficient Frequent Itemset Mining....Pages 458-463 Formal Approach and Automated Tool for Translating ER Schemata into OWL Ontologies....Pages 464-475 Separating Structure from Interestingness....Pages 476-485 Exploiting Recurring Usage Patterns to Enhance Filesystem and Memory Subsystem Performance....Pages 486-496 Automatic Text Extraction for Content-Based Image Indexing....Pages 497-507 Peculiarity Oriented Analysis in Multi-people Tracking Images....Pages 508-518 AutoSplit: Fast and Scalable Discovery of Hidden Variables in Stream and Multimedia Databases....Pages 519-528 Semantic Sequence Kin: A Method of Document Copy Detection....Pages 529-538 Extracting Citation Metadata from Online Publication Lists Using BLAST....Pages 539-548 Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models....Pages 549-558 Discovering Ordered Tree Patterns from XML Queries....Pages 559-563 Predicting Web Requests Efficiently Using a Probability Model....Pages 564-568 CCMine: Efficient Mining of Confidence-Closed Correlated Patterns....Pages 569-579 A Conditional Probability Distribution-Based Dissimilarity Measure for Categorial Data....Pages 580-589 Learning Hidden Markov Model Topology Based on KL Divergence for Information Extraction....Pages 590-594 A Non-parametric Wavelet Feature Extractor for Time Series Classification....Pages 595-603 Rules Discovery from Cross-Sectional Short-Length Time Series....Pages 604-614 Constraint-Based Mining of Formal Concepts in Transactional Data....Pages 615-624 Towards Optimizing Conjunctive Inductive Queries....Pages 625-637 Febrl – A Parallel Open Source Data Linkage System....Pages 638-647 A General Coding Method for Error-Correcting Output Codes....Pages 648-652 Discovering Partial Periodic Patterns in Discrete Data Sequences....Pages 653-658 Conceptual Mining of Large Administrative Health Data....Pages 659-669 A Semi-automatic System for Tagging Specialized Corpora....Pages 670-681 A Tree-Based Approach to the Discovery of Diagnostic Biomarkers for Ovarian Cancer....Pages 682-691 A Novel Parameter-Less Clustering Method for Mining Gene Expression Data....Pages 692-698 Extracting and Explaining Biological Knowledge in Microarray Data....Pages 699-703 Further Applications of a Particle Visualization Framework....Pages 704-710 Back Matter....Pages - This book constitutes the refereed proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data mining, PAKDD 2004, beld in Sydney, Australia in May 2004. The 50 revised full papers and 31 revised short papers presented were carefully reviewed and selected from a total of 238 submissions. The papers are organized in topical sections on classification; clustering; association rules; novel algorithms; event mining, anomaly detection, and intrusion detection; ensemble learning; Bayesian network and graph mining; text mining; multimedia mining; text mining and Web mining; statistical methods, sequential data mining, and time series mining; and biomedical data mining This book constitutes the refereed proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data mining, PAKDD 2004, held in Sydney, Australia in May 2004. The 50 revised full papers and 31 revised short papers presented were carefully reviewed and selected from a total of 238 submissions. The papers are organized in topical sections on classification; clustering; association rules; novel algorithms; event mining, anomaly detection, and intrusion detection; ensemble learning; Bayesian network and graph mining; text mining; multimedia mining; text mining and Web mining; statistical methods, sequential data mining, and time series mining; and biomedical data mining While some interesting progress has been achieved over the past few years, especially when it comes to techniques and scalable algorithms, very few organizations have managed to benefit from the technology.