Advances in Knowledge Discovery and Data Mining, Part I [electronic resource] : 14th Pacific-Asia Conference, PAKDD 2010, Hyderabat, India, June 21-24, 2010, Proceedings /

Keynote Speeches -- Empower People with Knowledge: The Next Frontier for Web Search -- Discovery of Patterns in Global Earth Science Data Using Data Mining -- Game Theoretic Approaches to Knowledge Discovery and Data Mining -- Session 1A. Clustering I -- A Set Correlation Model for Partitional Clustering -- iVAT and aVAT: Enhanced Visual Analysis for Cluster Tendency Assessment -- A Robust Seedless Algorithm for Correlation Clustering -- Integrative Parameter-Free Clustering of Data with Mixed Type Attributes -- Data Transformation for Sum Squared Residue -- Session 1B. Social Networks -- A Better Strategy of Discovering Link-Pattern Based Communities by Classical Clustering Methods -- Mining Antagonistic Communities from Social Networks -- As Time Goes by: Discovering Eras in Evolving Social Networks -- Online Sampling of High Centrality Individuals in Social Networks -- Estimate on Expectation for Influence Maximization in Social Networks -- Session 1C. Classification I -- A Novel Scalable Multi-class ROC for Effective Visualization and Computation -- Efficiently Finding the Best Parameter for the Emerging Pattern-Based Classifier PCL -- Rough Margin Based Core Vector Machine -- BoostML: An Adaptive Metric Learning for Nearest Neighbor Classification -- A New Emerging Pattern Mining Algorithm and Its Application in Supervised Classification -- Session 2A. Privacy -- Hiding Emerging Patterns with Local Recoding Generalization -- Anonymizing Transaction Data by Integrating Suppression and Generalization -- Satisfying Privacy Requirements: One Step before Anonymization -- Computation of Ratios of Secure Summations in Multi-party Privacy-Preserving Latent Dirichlet Allocation -- Privacy-Preserving Network Aggregation -- Multivariate Equi-width Data Swapping for Private Data Publication -- Session 2B.Spatio-Temporal Mining -- Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets -- Mining Trajectory Corridors Using Fréchet Distance and Meshing Grids -- Subseries Join: A Similarity-Based Time Series Match Approach -- TWave: High-Order Analysis of Spatiotemporal Data -- Spatial Clustering with Obstacles Constraints by Dynamic Piecewise-Mapped and Nonlinear Inertia Weights PSO -- Session 3A. Pattern Mining -- An Efficient GA-Based Algorithm for Mining Negative Sequential Patterns -- Valency Based Weighted Association Rule Mining -- Ranking Sequential Patterns with Respect to Significance -- Mining Association Rules in Long Sequences -- Mining Closed Episodes from Event Sequences Efficiently -- Most Significant Substring Mining Based on Chi-square Measure -- Session 3B. Recommendations/Answers -- Probabilistic User Modeling in the Presence of Drifting Concepts -- Using Association Rules to Solve the Cold-Start Problem in Recommender Systems -- Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems -- Cost-Sensitive Listwise Ranking Approach -- Mining Wikipedia and Yahoo! Answers for Question Expansion in Opinion QA -- Answer Diversification for Complex Question Answering on the Web -- Vocabulary Filtering for Term Weighting in Archived Question Search -- Session 3C. Topic Modeling/Information Extraction -- On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations -- Supervising Latent Topic Model for Maximum-Margin Text Classification and Regression -- Resource-Bounded Information Extraction: Acquiring Missing Feature Values on Demand -- Efficient Deep Web Crawling Using Reinforcement Learning -- Topic Decomposition and Summarization -- Session 4A. Skylines/Uncertainty -- UNN: A Neural Networkfor Uncertain Data Classification -- SkyDist: Data Mining on Skyline Objects -- Multi-Source Skyline Queries Processing in Multi-Dimensional Space -- Efficient Pattern Mining of Uncertain Data with Sampling -- Classifier Ensemble for Uncertain Data Stream Classification.