Advances in Knowledge Discovery and Data Mining, Part I (Paperback)

The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNAI has grown into the most comprehensive artificial intelligence research forum available.

The scope of LNAI spans the whole range of artificial intelligence and intelligent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes

-proceedings (published in time for the respective conference)

-post-proceedings (consisting of thoroughly revised final full papers)

-research monographs (which may be based on PhD work)

More recently, several color-cover sublines have been added featuring, beyond a collection of papers, various added-value components; these sublines include

-tutorials (textbook-like monographs or collections of lectures given at advanced courses)

-state-of-the-art surveys (offering complete and mediated coverage of a topic)

-hot topics (introducing emergent topics to the broader community)

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 Network for 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.