Advances in Knowledge Discovery and Data Mining, Part II

14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010, Proceedings
Herausgegeben:Zaki, Mohammed J.; Yu, Jeffrey Xu; Ravindran, B.; Pudi, Vikram

• Broschiertes Buch

Produktbeschreibung

The14thPaci?c-AsiaConferenceonKnowledgeDiscoveryandData Mining was held in Hyderabad, India during June 21 24, 2010; this was the ?rst time the conference was held in India. PAKDDisamajorinternationalconferenceintheareasofdatamining (DM) and knowledge discovery in databases (KDD). It provides an international - rum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scienti?c discovery, data visu- ization, causal induction and knowledge-based systems. PAKDD-2010 received 412 research papers from over 34 countries incl- ing: Australia,Austria,Belgium, Canada,China, Cuba, Egypt,Finland, France, Germany, Greece, Hong Kong, India, Iran, Italy, Japan, S. Korea, Malaysia, Mexico,TheNetherlands,NewCaledonia,NewZealand,SanMarino,Singapore, Slovenia,Spain, Switzerland, Taiwan, Thailand, Tunisia, Turkey, UK, USA, and Vietnam. This clearly re?ects the truly international stature of the PAKDD conference. AfteraninitialscreeningofthepapersbytheProgramCommitteeChairs,for papers that did not conform to the submission guidelines or that were deemed not worthy of further reviews, 60 papers were rejected with a brief expla- tion for the decision. The remaining 352 papers were rigorously reviewed by at least three reviewers. The initial results were discussed among the reviewers and ?nally judged by the Program Committee Chairs. In some cases of c- ?ict additional reviews were sought. As a result of the deliberation process, only 42 papers (10.2%) were accepted as long presentations (25 mins), and an ad- tional 55 papers (13.3%) were accepted as short presentations (15 mins). The total acceptance rate was thus about 23.5% across both categories.

Produktdetails

• Lecture Notes in Computer Science 6119
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 80016089, 978-3-642-13671-9
• 2010
• Seitenzahl: 544
• Erscheinungstermin: 1. Juni 2010
• Englisch
• Abmessung: 235mm x 155mm x 30mm
• Gewicht: 814g
• ISBN-13: 9783642136719
• ISBN-10: 3642136710
• Artikelnr.: 29841452

Inhaltsangabe

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.