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Advances in Knowledge Discovery and Data Mining

  • Kartonierter Einband
  • 879 Seiten
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The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the area of da... Weiterlesen
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The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the area of data mining and knowledge discovery. This year marks the tenth anniversary of the successful annual series of PAKDD conferences held in the Asia Pacific region. It was with pleasure that we hosted PAKDD 2006 in Singapore again, since the inaugural PAKDD conference was held in Singapore in 1997. PAKDD 2006 continues its tradition of providing an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all aspects of KDD data mining, including data cleaning, data warehousing, data mining techniques, knowledge visualization, and data mining applications. This year, we received 501 paper submissions from 38 countries and regions in Asia, Australasia, North America and Europe, of which we accepted 67 (13.4%) papers as regular papers and 33 (6.6%) papers as short papers. The distribution of the accepted papers was as follows: USA (17%), China (16%), Taiwan (10%), Australia (10%), Japan (7%), Korea (7%), Germany (6%), Canada (5%), Hong Kong (3%), Singapore (3%), New Zealand (3%), France (3%), UK (2%), and the rest from various countries in the Asia Pacific region.

Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining

Includes 67 revised full papers and 33 revised short papers presented together with 3 invited talks

Organized in topical sections on Classification, Ensemble Learning, Clustering, Support Vector Machines, Text and Document Mining and more

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 Sporadic Rules.- Improved Negative-Border Online Mining Approaches.- Association-Based Dissimilarity Measures for Categorical Data: Limitation and Improvement.- Is Frequency Enough for Decision Makers to Make Decisions?.- Ramp: High Performance Frequent Itemset Mining with Efficient Bit-Vector Projection Technique.- Evaluating a Rule Evaluation Support Method Based on Objective Rule Evaluation Indices.- Bio-data Mining.- Scoring Method for Tumor Prediction from Microarray Data Using an Evolutionary Fuzzy Classifier.- Efficient Discovery of Structural Motifs from Protein Sequences with Combination of Flexible Intra- and Inter-block Gap Constraints.- Finding Consensus Patterns in Very Scarce Biosequence Samples from Their Minimal Multiple Generalizations.- Kernels on Lists and Sets over Relational Algebra: An Application to Classification of Protein Fingerprints.- Mining Quantitative Maximal Hyperclique Patterns: A Summary of Results.- Outlier and Intrusion Detection.- A Nonparametric Outlier Detection for Effectively Discovering Top-N Outliers from Engineering Data.- A Fast Greedy Algorithm for Outlier Mining.- Ranking Outliers Using Symmetric Neighborhood Relationship.- Construction of Finite Automata for Intrusion Detection from System Call Sequences by Genetic Algorithms.- An Adaptive Intrusion Detection Algorithm Based on Clustering and Kernel-Method.- Weighted Intra-transactional Rule Mining for Database Intrusion Detection.- Privacy.- On Robust and Effective K-Anonymity in Large Databases.- Achieving Private Recommendations Using Randomized Response Techniques.- Privacy-Preserving SVM Classification on Vertically Partitioned Data.- Relational Database.- Data Mining Using Relational Database Management Systems.- Bias-Free Hypothesis Evaluation in Multirelational Domains.- Enhanced DB-Subdue: Supporting Subtle Aspects of Graph Mining Using a Relational Approach.- Multimedia Mining.- Multimedia Semantics Integration Using Linguistic Model.- A Novel Indexing Approach for Efficient and Fast Similarity Search of Captured Motions.- Mining Frequent Spatial Patterns in Image Databases.- Image Classification Via LZ78 Based String Kernel: A Comparative Study.- Stream Data Mining.- Distributed Pattern Discovery in Multiple Streams.- COMET: Event-Driven Clustering over Multiple Evolving Streams.- Variable Support Mining of Frequent Itemsets over Data Streams Using Synopsis Vectors.- Hardware Enhanced Mining for Association Rules.- A Single Index Approach for Time-Series Subsequence Matching That Supports Moving Average Transform of Arbitrary Order.- Efficient Mining of Emerging Events in a Dynamic Spatiotemporal Environment.- Temporal Data Mining.- A Multi-Hierarchical Representation for Similarity Measurement of Time Series.- Multistep-Ahead Time Series Prediction.- Sequential Pattern Mining with Time Intervals.- A Wavelet Analysis Based Data Processing for Time Series of Data Mining Predicting.- Temporal Data Mining.- Intelligent Particle Swarm Optimization in Multi-objective Problems.- Hidden Space Principal Component Analysis.- Neighbor Line-Based Locally Linear Embedding.- Predicting Rare Extreme Values.- Domain-Driven Actionable Knowledge Discovery in the Real World.- Evaluation of Attribute-Aware Recommender System Algorithms on Data with Varying Characteristics.- Innovative Applications.- An Intelligent System Based on Kernel Methods for Crop Yield Prediction.- A Machine Learning Application for Human Resource Data Mining Problem.- Towards Automated Design of Large-Scale Circuits by Combining Evolutionary Design with Data Mining.- Mining Unexpected Associations for Signalling Potential Adverse Drug Reactions from Administrative Health Databases.


Titel: Advances in Knowledge Discovery and Data Mining
Untertitel: 10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9-12, 2006, Proceedings
EAN: 9783540332060
ISBN: 978-3-540-33206-0
Format: Kartonierter Einband
Herausgeber: Springer-Verlag GmbH
Genre: Informatik
Anzahl Seiten: 879
Gewicht: 1345g
Größe: H238mm x B158mm x T48mm
Jahr: 2006
Untertitel: Englisch
Auflage: 2006. 2006

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