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