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Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference, PAKDD 2003. Seoul, Korea, April 30 - May 2, 2003, Proceedings de Kyu-Young Whang

Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference, PAKDD 2003. Seoul, Korea, April 30 - May 2, 2003, Proceedings: Lecture Notes in Computer Science, cartea 2637

Editat de Kyu-Young Whang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivatava
en Limba Engleză Paperback – 16 apr 2003
The 7th Paci?c Asia Conference on Knowledge Discovery and Data Mining (PAKDD) was held from April 30 to May 2, 2003 in the Convention and Ex- bition Center (COEX), Seoul, Korea. The PAKDD conference is a major forum for academic researchers and industry practitioners in the Paci?c Asia region to share original research results and development experiences from di?erent KDD-related areas such as data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and discovery, data visualization, and knowledge-based systems. The conference was organized by the Advanced Information Technology Research Center (AITrc) at KAIST and the Statistical Research Center for Complex Systems (SRCCS) at Seoul National University. It was sponsored by the Korean Datamining Society (KDMS), the Korea Inf- mation Science Society (KISS), the United States Air Force O?ce of Scienti?c Research, the Asian O?ce of Aerospace Research & Development, and KAIST. It was held with cooperation from ACM’s Special Group on Knowledge Dis- very and Data Mining (SIGKDD).
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Specificații

ISBN-13: 9783540047605
ISBN-10: 3540047603
Pagini: 636
Ilustrații: XVIII, 614 p.
Dimensiuni: 155 x 235 x 33 mm
Greutate: 0.84 kg
Ediția:2003
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Industrial Papers (Invited).- Data Mining as an Automated Service.- Trends and Challenges in the Industrial Applications of KDD.- Stream Mining I.- Finding Event-Oriented Patterns in Long Temporal Sequences.- Mining Frequent Episodes for Relating Financial Events and Stock Trends.- Graph Mining.- An Efficient Algorithm of Frequent Connected Subgraph Extraction.- Classifier Construction by Graph-Based Induction for Graph-Structured Data.- Clustering I.- Comparison of the Performance of Center-Based Clustering Algorithms.- Automatic Extraction of Clusters from Hierarchical Clustering Representations.- Text Mining.- Large Scale Unstructured Document Classification Using Unlabeled Data and Syntactic Information.- Extracting Shared Topics of Multiple Documents.- An Empirical Study on Dimensionality Optimization in Text Mining for Linguistic Knowledge Acquisition.- A Semi-supervised Algorithm for Pattern Discovery in Information Extraction from Textual Data.- Bio Mining.- Mining Patterns of Dyspepsia Symptoms Across Time Points Using Constraint Association Rules.- Predicting Protein Structural Class from Closed Protein Sequences.- Learning Rules to Extract Protein Interactions from Biomedical Text.- Predicting Protein Interactions in Human by Homologous Interactions in Yeast.- Web Mining.- Mining the Customer’s Up-To-Moment Preferences for E-commerce Recommendation.- A Graph-Based Optimization Algorithm for Website Topology Using Interesting Association Rules.- A Markovian Approach for Web User Profiling and Clustering.- Extracting User Interests from Bookmarks on the Web.- Stream Mining II.- Mining Frequent Instances on Workflows.- Real Time Video Data Mining for Surveillance Video Streams.- Distinguishing Causal and Acausal Temporal Relations.- Bayesian Networks.- OnlineBayes Point Machines.- Exploiting Hierarchical Domain Values for Bayesian Learning.- A New Restricted Bayesian Network Classifier.- Clustering II.- AGRID: An Efficient Algorithm for Clustering Large High-Dimensional Datasets.- Multi-level Clustering and Reasoning about Its Clusters Using Region Connection Calculus.- An Efficient Cell-Based Clustering Method for Handling Large, High-Dimensional Data.- Association Rules I.- Enhancing SWF for Incremental Association Mining by Itemset Maintenance.- Reducing Rule Covers with Deterministic Error Bounds.- Evolutionary Approach for Mining Association Rules on Dynamic Databases.- Semi-structured Data Mining.- Position Coded Pre-order Linked WAP-Tree for Web Log Sequential Pattern Mining.- An Integrated System of Mining HTML Texts and Filtering Structured Documents.- A New Sequential Mining Approach to XML Document Similarity Computation.- Classification I.- Optimization of Fuzzy Rules for Classification Using Genetic Algorithm.- Fast Pattern Selection for Support Vector Classifiers.- Averaged Boosting: A Noise-Robust Ensemble Method.- Improving Performance of Decision Tree Algorithms with Multi-edited Nearest Neighbor Rule.- Data Analysis.- HOT: Hypergraph-Based Outlier Test for Categorical Data.- A Method for Aggregating Partitions, Applications in K.D.D..- Efficiently Computing Iceberg Cubes with Complex Constraints through Bounding.- Extraction of Tag Tree Patterns with Contractible Variables from Irregular Semistructured Data.- Association Rules II.- Step-by-Step Regression: A More Efficient Alternative for Polynomial Multiple Linear Regression in Stream Cube.- Progressive Weighted Miner: An Efficient Method for Time-Constraint Mining.- Mining Open Source Software (OSS) Data Using Association Rules Network.- Parallel FP-Growth on PC Cluster.- Feature Selection.- Active Feature Selection Using Classes.- Electricity Based External Similarity of Categorical Attributes.- Weighted Proportional k-Interval Discretization for Naive-Bayes Classifiers.- Dealing with Relative Similarity in Clustering: An Indiscernibility Based Approach.- Stream Mining III.- Considering Correlation between Variables to Improve Spatiotemporal Forecasting.- Correlation Analysis of Spatial Time Series Datasets: A Filter-and-Refine Approach.- When to Update the Sequential Patterns of Stream Data?.- Clustering III.- A New Clustering Algorithm for Transaction Data via Caucus.- DBRS: A Density-Based Spatial Clustering Method with Random Sampling.- Optimized Clustering for Anomaly Intrusion Detection.- Classification II.- Finding Frequent Subgraphs from Graph Structured Data with Geometric Information and Its Application to Lossless Compression.- Upgrading ILP Rules to First-Order Bayesian Networks.- A Clustering Validity Assessment Index.

Caracteristici

Includes supplementary material: sn.pub/extras