Advances in Knowledge Discovery and Data Mining (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
864
Utgivningsdatum
2005-05-01
Upplaga
2005 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Li, Huan (ed.)
Illustratör/Fotograf
Bibliographie
Illustrationer
XXI, 864 p.
Dimensioner
234 x 156 x 45 mm
Vikt
1226 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783540260769
Advances in Knowledge Discovery and Data Mining (häftad)

Advances in Knowledge Discovery and Data Mining

9th Pacific-Asia Conference, PAKDD 2005, Hanoi, Vietnam, May 18-20, 2005, Proceedings

Häftad Engelska, 2005-05-01
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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. It provides an international forum 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 scientific discovery, data visualization, causality induction, and knowledge-based systems. This year's conference (PAKDD 2005) was the ninth of the PAKDD series, and carried the tradition in providing high-quality technical programs to facilitate research in knowledge discovery and data mining. It was held in Hanoi, Vietnam at the Melia Hotel, 18-20 May 2005. We are pleased to provide some statistics about PAKDD 2005. This year we received 327 submissions (a 37% increase over PAKDD 2004), which is the highest number of submissions since the first PAKDD in 1997) from 28 countries/regions: Australia (33), Austria (1), Belgium (2), Canada (11), China (91), Switzerland (2), France (9), Finland (1), Germany (5), Hong Kong (11), Indonesia (1), India (2), Italy (2), Japan (21), Korea (51), Malaysia (1), Macau (1), New Zealand (3), Poland (4), Pakistan (1), Portugal (3), Singapore (12), Taiwan (19), Thailand (7), Tunisia (2), UK (5), USA (31), and Vietnam (9). The submitted papers went through a rigorous reviewing process. Each submission was reviewed by at least two reviewers, and most of them by three or four reviewers.
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Innehållsförteckning

Keynote Speech and Invited Talks.- Machine Learning for Analyzing Human Brain Function.- Subgroup Discovery Techniques and Applications.- IT Development in the 21st Century and Its Implications.- Theoretic Foundations.- Data Mining of Gene Expression Microarray via Weighted Prefix Trees.- Automatic Extraction of Low Frequency Bilingual Word Pairs from Parallel Corpora with Various Languages.- A Kernel Function Method in Clustering.- Performance Measurements for Privacy Preserving Data Mining.- Extraction of Frequent Few-Overlapped Monotone DNF Formulas with Depth-First Pruning.- Association Rules.- Rule Extraction from Trained Support Vector Machines.- Pruning Derivative Partial Rules During Impact Rule Discovery.- : A New Informative Generic Base of Association Rules.- A Divide and Conquer Approach for Deriving Partially Ordered Sub-structures.- Finding Sporadic Rules Using Apriori-Inverse.- Automatic View Selection: An Application to Image Mining.- Pushing Tougher Constraints in Frequent Pattern Mining.- An Efficient Compression Technique for Frequent Itemset Generation in Association Rule Mining.- Mining Time-Profiled Associations: An Extended Abstract.- Online Algorithms for Mining Inter-stream Associations from Large Sensor Networks.- Mining Frequent Ordered Patterns.- Biomedical Domains.- Conditional Random Fields for Transmembrane Helix Prediction.- A DNA Index Structure Using Frequency and Position Information of Genetic Alphabet.- An Automatic Unsupervised Querying Algorithm for Efficient Information Extraction in Biomedical Domain.- Voting Fuzzy k-NN to Predict Protein Subcellular Localization from Normalized Amino Acid Pair Compositions.- Comparison of Tree Based Methods on Mammography Data.- Bayesian Sequence Learning for Predicting Protein Cleavage Points.- A Novel Indexing Method for Efficient Sequence Matching in Large DNA Database Environment.- Classification and Ranking.- Threshold Tuning for Improved Classification Association Rule Mining.- Using Rough Set in Feature Selection and Reduction in Face Recognition Problem.- Analysis of Company Growth Data Using Genetic Algorithms on Binary Trees.- Considering Re-occurring Features in Associative Classifiers.- A New Evolutionary Neural Network Classifier.- A Privacy-Preserving Classification Mining Algorithm.- Combining Classifiers with Multi-representation of Context in Word Sense Disambiguation.- Automatic Occupation Coding with Combination of Machine Learning and Hand-Crafted Rules.- Retrieval Based on Language Model with Relative Entropy and Feedback.- Text Classification for DAG-Structured Categories.- Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees.- Improving Rough Classifiers Using Concept Ontology.- QED: An Efficient Framework for Temporal Region Query Processing.- Clustering.- A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams.- Locating Motifs in Time-Series Data.- Stochastic Local Clustering for Massive Graphs.- A Neighborhood-Based Clustering Algorithm.- Improved Self-splitting Competitive Learning Algorithm.- Speeding-Up Hierarchical Agglomerative Clustering in Presence of Expensive Metrics.- Dynamic Cluster Formation Using Level Set Methods.- A Vector Field Visualization Technique for Self-organizing Maps.- Visualization of Cluster Changes by Comparing Self-organizing Maps.- An Incremental Data Stream Clustering Algorithm Based on Dense Units Detection.- Visual Interactive Evolutionary Algorithm for High Dimensional Data Clustering and Outlier Detection.- Approximated Clustering of Distributed High-Dimensional Data.- Dynamic Data Mining.- Improvements of IncSpan: Incremental Mining of Sequential Patterns in Large Database.- Efficient Sampling: Application to Image Data.- Cluster-Based Rough Set Construction.- Graphic Model Discovery.- Learning Bayesian Networks Structures from Incomplete Data: An Efficient Approach Based on Extended Evolutionary Programming.-