Advances in Knowledge Discovery and Data Mining (häftad)
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Häftad (Paperback / softback)
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2006 ed.
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
8Ng, Wee Keong
XXIV, 879 p.
234 x 156 x 46 mm
1253 g
Antal komponenter
1 Paperback / softback
Advances in Knowledge Discovery and Data Mining (häftad)

Advances in Knowledge Discovery and Data Mining

10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9-12, 2006, Proceedings

Häftad Engelska, 2006-03-01
Skickas inom 3-6 vardagar.
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This book constitutes the refereed proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2006, held in Singapore in April 2006. The 67 revised full papers and 33 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 501 submissions. The papers are organized in topical sections on Classification, Ensemble Learning, Clustering, Support Vector Machines, Text and Document Mining, Web Mining, Bio-Data Mining, and more.
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Keynote Speech.- Protection or Privacy? Data Mining and Personal Data.- The Changing Face of Web Search.- Invited Speech.- Data Mining for Surveillance Applications.- Classification.- A Multiclass Classification Method Based on Output Design.- Regularized Semi-supervised Classification on Manifold.- Similarity-Based Sparse Feature Extraction Using Local Manifold Learning.- Generalized Conditional Entropy and a Metric Splitting Criterion for Decision Trees.- RNBL-MN: A Recursive Naive Bayes Learner for Sequence Classification.- TRIPPER: Rule Learning Using Taxonomies.- Using Weighted Nearest Neighbor to Benefit from Unlabeled Data.- Constructive Meta-level Feature Selection Method Based on Method Repositories.- Ensemble Learning.- Variable Randomness in Decision Tree Ensembles.- Further Improving Emerging Pattern Based Classifiers Via Bagging.- Improving on Bagging with Input Smearing.- Boosting Prediction Accuracy on Imbalanced Datasets with SVM Ensembles.- Ensemble Learning.- DeLi-Clu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking.- Iterative Clustering Analysis for Grouping Missing Data in Gene Expression Profiles.- An EM-Approach for Clustering Multi-Instance Objects.- Mining Maximal Correlated Member Clusters in High Dimensional Database.- Hierarchical Clustering Based on Mathematical Optimization.- Clustering Multi-represented Objects Using Combination Trees.- Parallel Density-Based Clustering of Complex Objects.- Neighborhood Density Method for Selecting Initial Cluster Centers in K-Means Clustering.- Uncertain Data Mining: An Example in Clustering Location Data.- Support Vector Machines.- Parallel Randomized Support Vector Machine.- ?-Tube Based Pattern Selection for Support Vector Machines.- Self-adaptive Two-Phase Support Vector Clustering for Multi-Relational Data Mining.- One-Class Support Vector Machines for Recommendation Tasks.- Text and Document Mining.- Heterogeneous Information Integration in Hierarchical Text Classification.- FISA: Feature-Based Instance Selection for Imbalanced Text Classification.- Dynamic Category Profiling for Text Filtering and Classification.- Detecting Citation Types Using Finite-State Machines.- A Systematic Study of Parameter Correlations in Large Scale Duplicate Document Detection.- Comparison of Documents Classification Techniques to Classify Medical Reports.- XCLS: A Fast and Effective Clustering Algorithm for Heterogenous XML Documents.- Clustering Large Collection of Biomedical Literature Based on Ontology-Enriched Bipartite Graph Representation and Mutual Refinement Strategy.- Web Mining.- Level-Biased Statistics in the Hierarchical Structure of the Web.- Cleopatra: Evolutionary Pattern-Based Clustering of Web Usage Data.- Extracting and Summarizing Hot Item Features Across Different Auction Web Sites.- Clustering Web Sessions by Levels of Page Similarity.- i Wed: An Integrated Multigraph Cut-Based Approach for Detecting Events from a Website.- Enhancing Duplicate Collection Detection Through Replica Boundary Discovery.- Graph and Network Mining.- Summarization and Visualization of Communication Patterns in a Large-Scale Social Network.- Patterns of Influence in a Recommendation Network.- Constructing Decision Trees for Graph-Structured Data by Chunkingless Graph-Based Induction.- Combining Smooth Graphs with Semi-supervised Classification.- Network Data Mining: Discovering Patterns of Interaction Between Attributes.- Association Rule Mining.- SGPM: Static Group Pattern Mining Using Apriori-Like Sliding Window.- Mining Temporal Indirect Associations.- Mining Top-K Frequent Closed Itemsets Is Not in APX.- Quality-Aware Association Rule Mining.- IMB3-Miner: Mining Induced/Embedded Subtrees by Constraining the Level of Embedding.- Maintaining Frequent Itemsets over High-Speed Data Streams.- Generalized Disjunction-Free Representation of Frequents Patterns with at Most k Negations.- Mining Interesting Imperfectly S