Advances in Knowledge Discovery and Data Mining : 6th Pacific-Asia Conference, Pakdd 2002, Taipei, Taiwan, May 6-8, 2002 : Proceedings

Liu, Bing (EDT); Yu, Philip S. (EDT)

ISBN 10: 3540437045 / ISBN 13: 9783540437048

Editore: Springer, 2002

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Codice articolo 980788-n

Dati bibliografici

• Titolo: Advances in Knowledge Discovery and Data Mining : 6th Pacific-Asia Conference, Pakdd 2002, Taipei, Taiwan, May 6-8, 2002 : Proceedings
• Editore: Springer
• Data di pubblicazione: 2002
• Lingua: Inglese
• ISBN 10: 3540437045
• ISBN 13: 9783540437048
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

This book constitutes the refereed proceedings of the 6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2002, held in Taipei, Taiwan, in May 2002. The 32 revised full papers and 20 short papers presented together with 4 invited contributions were carefully reviewed and selected from a total of 128 submissions. The papers are organized in topical sections on association rules; classification; interestingness; sequence mining; clustering; Web mining; semi-structure and concept mining; data warehouse and data cube; bio-data mining; temporal mining; and outliers, missing data, and causation.

Contenuti

Industrial Papers (Invited).- Network Data Mining and Analysis: The Project.- Privacy Preserving Data Mining: Challenges and Opportunities.- Survey Papers (Invited).- A Case for Analytical Customer Relationship Management.- On Data Clustering Analysis: Scalability, Constraints, and Validation.- Association Rules (I).- Discovering Numeric Association Rules via Evolutionary Algorithm.- Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining.- Association Rule Mining on Remotely Sensed Images Using P-trees.- On the Efficiency of Association-Rule Mining Algorithms.- Classification (I).- A Function-Based Classifier Learning Scheme Using Genetic Programming.- SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning.- A Method to Boost Naïve Bayesian Classifiers.- Toward Bayesian Classifiers with Accurate Probabilities.- Interestingness.- Pruning Redundant Association Rules Using Maximum Entropy Principle.- A Confidence-Lift Support Specification for Interesting Associations Mining.- Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators.- Mining Interesting Association Rules: A Data Mining Language.- The Lorenz Dominance Order as a Measure of Interestingness in KDD.- Sequence Mining.- Efficient Algorithms for Incremental Update of Frequent Sequences.- DELISP: Efficient Discovery of Generalized Sequential Patterns by Delimited Pattern-Growth Technology.- Self-Similarity for Data Mining and Predictive Modeling A Case Study for Network Data.- A New Mechanism of Mining Network Behavior.- Clustering.- M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining.- An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory.- Adding Personality to Information Clustering.- Clustering Large Categorical Data.- Web Mining.- WebFrame: In Pursuit of Computationally and Cognitively Efficient Web Mining.- Naviz:Website Navigational Behavior Visualizer.- Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs.- Automatic Information Extraction for Multiple Singular Web Pages.- Association Rules (II).- An Improved Approach for the Discovery of Causal Models via MML.- SETM*-MaxK: An Efficient SET-Based Approach to Find the Largest Itemset.- Discovery of Ordinal Association Rules.- Value Added Association Rules.- Top Down FP-Growth for Association Rule Mining.- Semi-structure & Concept Mining.- Discovery of Frequent Tag Tree Patterns in Semistructured Web Documents.- Extracting Characteristic Structures among Words in Semistructured Documents.- An Efficient Algorithm for Incremental Update of Concept Spaces.- Data Warehouse and Data Cube.- Efficient Constraint-Based Exploratory Mining on Large Data Cubes.- Efficient Utilization of Materialized Views in a Data Warehouse.- Bio-Data Mining.- Mining Interesting Rules in Meningitis Data by Cooperatively Using GDT-RS and RSBR.- Evaluation of Techniques for Classifying Biological Sequences.- Efficiently Mining Gene Expression Data via Integrated Clustering and Validation Techniques.- Classification (II).- Adaptive Generalized Estimation Equation with Bayes Classifier for the Job Assignment Problem.- GEC: An Evolutionary Approach for Evolving Classifiers.- An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification.- A Method to Boost Support Vector Machines.- Temporal Mining.- Distribution Discovery: Local Analysis of Temporal Rules.- News Sensitive Stock Trend Prediction.- User Profiling for Intrusion Detection Using Dynamic and Static Behavioral Models.- Classification (III).- Incremental Extraction of Keyterms for Classifying Multilingual Documents in the Web.- k-nearest Neighbor Classification on Spatial Data Streams Using P-trees.- Interactive Construction of Classification Rules.- Outliers, Missing Data, and Causation.- Enhancing Effectiveness of Outlier Detections for Low Density Patterns.- Cluster-Based Algorithms for Dealing with Missing Values.- Extracting Causation Knowledge from Natural Language Texts.- Mining Relationship Graphs for Effective Business Objectives.