Advances in Knowledge Discovery and Data Mining, Part II: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010, Proceedings

Advances in Knowledge Discovery and Data Mining, Part II: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010, Proceedings


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Product Details

ISBN-13: 9783642136719
Publisher: Springer Berlin Heidelberg
Publication date: 07/21/2010
Series: Lecture Notes in Computer Science , #6119
Edition description: 2010
Pages: 520
Product dimensions: 6.10(w) x 9.20(h) x 1.10(d)

Table of Contents

Session 4B. Dimensionality Reduction/Parallelism.- Subclass-Oriented Dimension Reduction with Constraint Transformation and Manifold Regularization.- Distributed Knowledge Discovery with Non Linear Dimensionality Reduction.- DPSP: Distributed Progressive Sequential Pattern Mining on the Cloud.- An Approach for Fast Hierarchical Agglomerative Clustering Using Graphics Processors with CUDA.- Session 5A. Novel Applications.- Ontology-Based Mining of Brainwaves: A Sequence Similarity Technique for Mapping Alternative Features in Event-Related Potentials (ERP) Data.- Combining Support Vector Machines and the t-statistic for Gene Selection in DNA Microarray Data Analysis.- Satrap: Data and Network Heterogeneity Aware P2P Data-Mining.- Player Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs).- Relevant Gene Selection Using Normalized Cut Clustering with Maximal Compression Similarity Measure.- Session 5B. Feature Selection/Visualization.- A Novel Prototype Reduction Method for the K-Nearest Neighbor Algorithm with K???1.- Generalized Two-Dimensional FLD Method for Feature Extraction: An Application to Face Recognition.- Learning Gradients with Gaussian Processes.- Analyzing the Role of Dimension Arrangement for Data Visualization in Radviz.- Session 6A. Graph Mining.- Subgraph Mining on Directed and Weighted Graphs.- Finding Itemset-Sharing Patterns in a Large Itemset-Associated Graph.- A Framework for SQL-Based Mining of Large Graphs on Relational Databases.- Fast Discovery of Reliable k-terminal Subgraphs.- GTRACE2: Improving Performance Using Labeled Union Graphs.- Session 6B. Clustering II.- Orthogonal Nonnegative Matrix Tri-factorization for Semi-supervised Document Co-clustering.- Rule Synthesizing from Multiple Related Databases.- Fast Orthogonal Nonnegative Matrix Tri-Factorization for Simultaneous Clustering.- Hierarchical Web-Page Clustering via In-Page and Cross-Page Link Structures.- Mining Numbers in Text Using Suffix Arrays and Clustering Based on Dirichlet Process Mixture Models.- Session 7A. Opinion/Sentiment Mining.- Opinion-Based Imprecise Query Answering.- Blog Opinion Retrieval Based on Topic-Opinion Mixture Model.- Feature Subsumption for Sentiment Classification in Multiple Languages.- Decentralisation of ScoreFinder: A Framework for Credibility Management on User-Generated Contents.- Classification and Pattern Discovery of Mood in Weblogs.- Capture of Evidence for Summarization: An Application of Enhanced Subjective Logic.- Session 7B. Stream Mining.- Fast Perceptron Decision Tree Learning from Evolving Data Streams.- Classification and Novel Class Detection in Data Streams with Active Mining.- Bulk Loading Hierarchical Mixture Models for Efficient Stream Classification.- Summarizing Multidimensional Data Streams: A Hierarchy-Graph-Based Approach.- Efficient Trade-Off between Speed Processing and Accuracy in Summarizing Data Streams.- Subsequence Matching of Stream Synopses under the Time Warping Distance.- Session 8A. Similarity and Kernels.- Normalized Kernels as Similarity Indices.- Adaptive Matching Based Kernels for Labelled Graphs.- A New Framework for Dissimilarity and Similarity Learning.- Semantic-Distance Based Clustering for XML Keyword Search.- Session 8B. Graph Analysis.- oddball: Spotting Anomalies in Weighted Graphs.- Robust Outlier Detection Using Commute Time and Eigenspace Embedding.- EigenSpokes: Surprising Patterns and Scalable Community Chipping in Large Graphs.- BASSET: Scalable Gateway Finder in Large Graphs.- Session 8C. Classification II.- Ensemble Learning Based on Multi-Task Class Labels.- Supervised Learning with Minimal Effort.- Generating Diverse Ensembles to Counter the Problem of Class Imbalance.- Relationship between Diversity and Correlation in Multi-Classifier Systems.- Compact Margin Machine.

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