Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings

ISBN-13: 9783540220640 / Angielski / Miękka / 2004 / 740 str.

H. Dai;Pacific-Asia Conference on Knowledge Discovery and Data Mini

Opis

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.

Spis treści

Invited Speeches.- Mining of Evolving Data Streams with Privacy Preservation.- Data Mining Grand Challenges.- Session 1A: Classification (I).- Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms.- Spectral Energy Minimization for Semi-supervised Learning.- Discriminative Methods for Multi-labeled Classification.- Session 1B: Clustering (I).- Subspace Clustering of High Dimensional Spatial Data with Noises.- Constraint-Based Graph Clustering through Node Sequencing and Partitioning.- Mining Expressive Process Models by Clustering Workflow Traces.- Session 1C: Association Rules (I).- CMTreeMiner: Mining Both Closed and Maximal Frequent Subtrees.- Secure Association Rule Sharing.- Self-Similar Mining of Time Association Rules.- Session 2A: Novel Algorithms (I).- ParaDualMiner: An Efficient Parallel Implementation of the DualMiner Algorithm.- A Novel Distributed Collaborative Filtering Algorithm and Its Implementation on P2P Overlay Network.- An Efficient Algorithm for Dense Regions Discovery from Large-Scale Data Streams.- Blind Data Linkage Using n-gram Similarity Comparisons.- Condensed Representation of Emerging Patterns.- Session 2B: Association (II).- Discovery of Maximally Frequent Tag Tree Patterns with Contractible Variables from Semistructured Documents.- Mining Term Association Rules for Heuristic Query Construction.- FP-Bonsai: The Art of Growing and Pruning Small FP-Trees.- Mining Negative Rules Using GRD.- Applying Association Rules for Interesting Recommendations Using Rule Templates.- Session 2C: Classification (II).- Feature Extraction and Classification System for Nonlinear and Online Data.- A Metric Approach to Building Decision Trees Based on Goodman-Kruskal Association Index.- DRC-BK: Mining Classification Rules with Help of SVM.- A New Data Mining Method Using Organizational Coevolutionary Mechanism.- Noise Tolerant Classification by Chi Emerging Patterns.- The Application of Emerging Patterns for Improving the Quality of Rare-Class Classification.- Session 3A: Event Mining, Anomaly Detection, and Intrusion Detection.- Finding Negative Event-Oriented Patterns in Long Temporal Sequences.- OBE: Outlier by Example.- Temporal Sequence Associations for Rare Events.- Summarization of Spacecraft Telemetry Data by Extracting Significant Temporal Patterns.- An Extended Negative Selection Algorithm for Anomaly Detection.- Adaptive Clustering for Network Intrusion Detection.- Session 3B: Ensemble Learning.- Ensembling MML Causal Discovery.- Logistic Regression and Boosting for Labeled Bags of Instances.- Fast and Light Boosting for Adaptive Mining of Data Streams.- Compact Dual Ensembles for Active Learning.- On the Size of Training Set and the Benefit from Ensemble.- Session 3C: Bayesian Network and Graph Mining.- Identifying Markov Blankets Using Lasso Estimation.- Selective Augmented Bayesian Network Classifiers Based on Rough Set Theory.- Using Self-Consistent Naive-Bayes to Detect Masquerades.- DB-Subdue: Database Approach to Graph Mining.- Session 3D: Text Mining (I).- Finding Frequent Structural Features among Words in Tree-Structured Documents.- Exploring Potential of Leave-One-Out Estimator for Calibration of SVM in Text Mining.- Classifying Text Streams in the Presence of Concept Drifts.- Using Cluster-Based Sampling to Select Initial Training Set for Active Learning in Text Classification.- Spectral Analysis of Text Collection for Similarity-Based Clustering.- Session 4A: Clustering (II).- Clustering Multi-represented Objects with Noise.- Providing Diversity in K-Nearest Neighbor Query Results.- Cluster Structure of K-means Clustering via Principal Component Analysis.- Combining Clustering with Moving Sequential Pattern Mining: A Novel and Efficient Technique.- An Alternative Methodology for Mining Seasonal Pattern Using Self-Organizing Map.- Session 4B: Association (III).- ISM: Item Selection for Marketing with Cross-Selling Considerations.- Efficient Pattern-Growth Methods for Frequent Tree Pattern Mining.- Mining Association Rules from Structural Deltas of Historical XML Documents.- Data Mining Proxy: Serving Large Number of Users for Efficient Frequent Itemset Mining.- Session 4C: Novel Algorithms (II).- Formal Approach and Automated Tool for Translating ER Schemata into OWL Ontologies.- Separating Structure from Interestingness.- Exploiting Recurring Usage Patterns to Enhance Filesystem and Memory Subsystem Performance.- Session 4D: Multimedia Mining.- Automatic Text Extraction for Content-Based Image Indexing.- Peculiarity Oriented Analysis in Multi-people Tracking Images.- AutoSplit: Fast and Scalable Discovery of Hidden Variables in Stream and Multimedia Databases.- Session 5A: Text Mining and Web Mining (II).- Semantic Sequence Kin: A Method of Document Copy Detection.- Extracting Citation Metadata from Online Publication Lists Using BLAST.- Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models.- Discovering Ordered Tree Patterns from XML Queries.- Predicting Web Requests Efficiently Using a Probability Model.- Session 5B: Statistical Methods, Sequential Data Mining, and Time Series Mining.- CCMine: Efficient Mining of Confidence-Closed Correlated Patterns.- A Conditional Probability Distribution-Based Dissimilarity Measure for Categorial Data.- Learning Hidden Markov Model Topology Based on KL Divergence for Information Extraction.- A Non-parametric Wavelet Feature Extractor for Time Series Classification.- Rules Discovery from Cross-Sectional Short-Length Time Series.- Session 5C: Novel Algorithms (III).- Constraint-Based Mining of Formal Concepts in Transactional Data.- Towards Optimizing Conjunctive Inductive Queries.- Febrl – A Parallel Open Source Data Linkage System.- A General Coding Method for Error-Correcting Output Codes.- Discovering Partial Periodic Patterns in Discrete Data Sequences.- Session 5D: Biomedical Mining.- Conceptual Mining of Large Administrative Health Data.- A Semi-automatic System for Tagging Specialized Corpora.- A Tree-Based Approach to the Discovery of Diagnostic Biomarkers for Ovarian Cancer.- A Novel Parameter-Less Clustering Method for Mining Gene Expression Data.- Extracting and Explaining Biological Knowledge in Microarray Data.- Further Applications of a Particle Visualization Framework.