Advances in Knowledge Discovery and Data Mining, Part I
Springer Berlin (Verlag)
978-3-642-13656-6 (ISBN)
- Lieferbar
- Versandkostenfrei
- Auch auf Rechnung
- Artikel merken
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.
| Erscheint lt. Verlag | 1.6.2010 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | 506 p. 167 illus. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Schlagworte | Algorithm analysis and problem complexity • Bioinformatics • classification • Clustering • Data Mining • Distributed data mining • fractal representation • graph analysis • Knowledge Discovery |
| ISBN-10 | 3-642-13656-7 / 3642136567 |
| ISBN-13 | 978-3-642-13656-6 / 9783642136566 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
