Advances in Knowledge Discovery and Data Mining de Ming-Syan Cheng

Advances in Knowledge Discovery and Data Mining

Ming-Syan Cheng

Philip S. Yu

Bing Liu

en Limba Engleză Paperback – 26 apr 2002

Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. In view of this, and following the success of the five previous PAKDD conferences, the sixth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2002) aimed to provide a forum for the sharing of original research results, innovative ideas, state-of-the-art developments, and implementation experiences in knowledge discovery and data mining among researchers in academic and industrial organizations. Much work went into preparing a program of high quality. We received 128 submissions. Every paper was reviewed by 3 program committee members, and 32 were selected as regular papers and 20 were selected as short papers, representing a 25% acceptance rate for regular papers. The PAKDD 2002 program was further enhanced by two keynote speeches, delivered by Vipin Kumar from the Univ. of Minnesota and Rajeev Rastogi from AT&T. In addition, PAKDD 2002 was complemented by three tutorials, XML and data mining (by Kyuseok Shim and Surajit Chadhuri), mining customer data across various customer touchpoints at- commerce sites (by Jaideep Srivastava), and data clustering analysis, from simple groupings to scalable clustering with constraints (by Osmar Zaiane and Andrew Foss).

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Cuprins

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 toInformation 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.