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Proceedings of the Ninth SIAM International Conference on Data Mining

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$180.00
Order Code:
PR133

 Product Description

Haesun Park, Srinivasan Parthasarathy, Huan Liu, and Zoran Obradovic, Editors

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2009 / 1244 pages / CD / ISBN: 978-0-898716-82-5 / List Price $180.00 /SIAM Member Price $126.00 / Order Code PR133

Symposium held in Sparks, NV, April 30-May 2, 2009.

Contents
GAD: General Activity Detection for Fast Clustering on Large Data; CORE: Nonparametric Clustering of Large Numeric Databases; Constraint-Based Subspace Clustering; Integrated KL (K-means – Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations; Hybrid Clustering of Text Mining and Bibliometrics Applied to Journal Sets; Event Discovery in Time Series; FuncICA for Time Series Pattern Discovery; Autocannibalistic and Anyspace Indexing Algorithms with Application to Sensor Data Mining; Proximity-Based Anomaly Detection Using Sparse Structure Learning; Optimal Distance Bounds on Time-Series Data; Application of Bayesian Partition Models in Warranty Data Analysis; Learning Random-Walk Kernels for Protein Remote Homology Identification and Motif Discovery; Outlier Detection with Globally Optimal Exemplar-Based GMM; Prior-Free Rare Category Detection; A Family of Large Margin Linear Classifiers and Its Application in Dynamic Environments; Unsupervised Learning and Clustering; DensEst: Density Estimation for Data Mining in High Dimensional Spaces; A Framework for Exploring Categorical Data; Discovering Substantial Distinctions among Incremental; Agglomerative Mean-Shift Clustering via Query Set Compression; Adaptive Concept Drift Detection; Scalable Distributed Change Detection from Astronomy Data Streams Using Local, Asynchronous Eigen Monitoring Algorithms; Positive Unlabeled Learning for Data Stream Classification; Time-Decayed Correlated Aggregates over Data Streams; Multi-Modal Hierarchical Dirichlet Process Model for Predicting Image Annotation and Image-Object Label Correspondence; A Bayesian Approach to Graphy Regression with Relevant Subgraph Selection; A Hybrid Data Mining Metaheuristic for the p-Median Problem; A New Constraint for Mining Sets in Sequences; A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning; A Semi-Supervised Framework for Feature Mapping and Multiclass Classification; Aligned Graph Classification with Regularized Logistic Regression; An Entity Based Model for Coreference Resolution; Analyses for Service Interaction Networks with Applications to Service Delivery; Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation; Context Aware Trace Clustering: Towards Improving Process Mining Results; Detection and Characterization of Anomalies in Multivariate Time Series; Discovery of Geospatial Discriminating Patterns from Remote Sensing Datasets; Diversity-Based Weighting Schemes for Clustering Ensembles; Divide and Conquer Strategies for Effective Information Retrieval; Speeding Up Secure Computations via Embedded Caching; Exact Discovery of Time Series Motifs; Exploiting Semantic Constraints for Estimating Supersenses with CRFs; Feature Weighted SVMs Using Receiver Operating Characteristics; FEDRA: A Fast and Efficient Dimensionality Reduction Algorithm; Finding Representative Association Rules from Large Rule Collections; FutureRank: Ranking Scientific Articles by Predicting their Future PageRank; Highlighting Diverse Concepts in Documents; Identifying Information-Rich Subspace Trends in High-Dimensional Data; Low-Entropy Set Selection; Measuring Discrimination in Socially-Sensitive Decision Records; Mining Cohesive Patterns from Graphs with Feature Vectors; Mining Complex Spatio-Temporal Sequence Patterns; Mining for Surprise Events Within Text Streams; Multi-field Correlated Topic Modeling; Multiple Kernel Clustering; MUSK: Uniform Sampling of k Maximal Patterns; Noise Robust Classification Based on Spread Spectrum; Non-negative Matrix Factorization, Convexity and Isometry; Non-parametric Information-Theoretic Measures of One-Dimensional Distribution Functions from Continuous Time Series; On Maximum Coverage in the Streaming Model & Application to Multi-topic Blog-Watch; On Randomness Measures for Social Networks; On Segment-Based Stream Modeling and Its Applications; On the Comparison of Relative Clustering Validity Criteria; Parallel Pairwise Clustering; PICC Counting: Who Needs Joins When You Can Propagate Efficiently?; Providing Privacy through Plausibly Deniable Search; Randomization Techniques for Graphs; Semi-supervised Learning by Sparse Representation; ShatterPlots: Fast Tools for Mining Large Graphs; Spatially Cost-Sensitive Active Learning; Structure and Dynamics of Research Collaboration in Computer Science; Text Categorization with All Substring Features; The Set Classification Problem and Solution Methods; Topic Evolution in a Stream of Documents; Tracking User Mobility to Detect Suspicious Behavior; Toward Optimal Ordering of Prediction Tasks; Hierarchical Linear Discriminant Analysis for Beamforming; Twin Vector Machines for Online Learning on a Budget; The Metric Dilemma: Competence-Conscious Associative Classification; AMORI: A Metric-Based One Rule Inducer; Identifying Unsafe Routes for Network-Based Trajectory Privacy; Privacy Preservation in Social Networks with Sensitive Edge Weights; Graph Generation with Prescribed Feature Constraints; Detecting Communities in Social Networks Using Max-Min Modularity; A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks; Efficient Discovery of Interesting Patterns Based on Strong Closedness; Efficient Computation of Partial-Support for Mining Interesting Itemsets; Grammar Mining; Top-k Correlative Graph Mining; High Performance Parallel/Distributed Biclustering Using Barycenter Heuristic; MultiVis: Content-Based Social Network Exploration through Multi-way Visual Analysis; Near-Optimal Supervised Feature Selection among Frequent Subgraphs; Polynomial-Delay and Polynomial-Space Algorithms for Mining Closed Sequences, Graphs, and Pictures in Accessible Set Systems; Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction; Understanding Importance of Collaborations in Co-authorship Networks: A Supportiveness Analysis Approach; Topic Cube: Topic Modeling for OLAP on Multidimensional Text Databases; Local Relevance Weighted Maximum Margin Criterion for Text Classification; Multi-topic Based Query-Oriented Summarization; Straightforward Feature Selection for Scalable Latent Semantic Indexing; Parallel Large Scale Feature Selection for Logistic Regression; Travel-Time Prediction Using Gaussian Process Regression: A Trajectory-Based Approach; Discretized Spatio-Temporal Scan Window; Finding Links and Initiators: A Graph-Reconstruction Problem; Efficient Multiplicative Updates for Support Vector Machines; Efficient Active Learning with Boosting; Author Index.

 

ISBN: 9780898716825

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