Note: Intro -- Advances in Knowledge Discovery and Data Mining -- Preface -- PAKDD 2002 Conference Committee -- PAKDD 2002 Program Committee -- Table of Contents -- Network Data Mining and Analysis: The NEMESIS Project -- Introduction -- Model-Based Semantic Compression for Network-Data Tables -- Overview of Approach -- SPARTAN System Architecture -- Data Mining Techniques for Network-Fault Management -- Conclusions -- References -- Privacy Preserving Data Mining: Challenges and Opportunities -- A Case for Analytical Customer Relationship Management -- Introduction -- Analytical Customer Relationship Management -- Customer Segmentation -- Customer Communication -- Customer Retention -- Customer Loyalty -- Data Analytics Support for Analytical CRM -- Data Analytics Architecture -- Data Warehouse -- Data Mining -- Organizational Issues in Analytical CRM Adoption -- Customer First' Orientation -- Attention to Data Aspects of Analytical CRM -- Organizational 'Buy In' -- Incremental Introduction of CRM -- Conclusion -- References -- On Data Clustering Analysis: Scalability, Constraints, and Validation -- Introduction -- Taxonomy on Clustering Techniques -- Partitioning Methods -- Hierarchical Methods -- Density-Based Methods -- Grid-Based Methods -- Clustering Spatial Data in Presence of Constraints -- Constraint-Based Clustering -- Conclusion: Clustering Validation -- References -- Discovering Numeric Association Rules via Evolutionary Algorithm -- Introduction -- A Motivation Example -- Preliminaries -- Practical Implementation -- GAR Algorithm -- Structure of Individuals -- Initial Population -- Genetic Operators -- Fitness Function -- Experimental Results -- Synthetic Databases -- Real-Life Databases -- Conclusions -- Acknowledgments -- References -- Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining -- Introduction. , Mining for Association Rules -- Motivation -- Contribution and Outline of This Paper -- Interactivity Through Caching and Efficient Retrieval -- Basic Idea -- Enhanced Rule Retrieval -- Postponing Restrict Operations on the Mining Data -- Restricting the Mining Data -- Postponing Restrict Operations to Retrieval -- The SMemph {ART}SKIP System -- Implementation -- Evaluation -- Conclusion -- References -- Association Rule Mining on Remotely Sensed Images Using P-trees -- Introduction -- Peano Count Tree (P-tree) -- RSI Data Formats -- Basic P-trees -- P-tree Operations -- Value, Tuple, Interval, and Cube P-trees -- Association Rule Mining on Spatial Data Using P-trees -- Data Partitioning for Numeric Data -- Deriving Association Rules Using P-trees -- Pruning Techniques -- An Example -- Experiment Results and Performance Analysis -- Comparison of the P-ARM with Apriori -- Comparison of the P-ARM Algorithm and the FP-growth Algorithm -- Related Work and Discussions -- Conclusion and Future Work -- Acknowledgement -- References -- On the Efficiency of Association-Rule Mining Algorithms -- Introduction -- The Oracle Algorithm -- The Mechanics of Oracle -- Optimality of Oracle -- The ARMOR Algorithm -- Memory Utilization in ARMOR -- Performance Study -- Discussion of Experimental Results -- Conclusions -- References -- A Function-Based Classifier Learning Scheme Using Genetic Programming -- Introduction -- Related Works -- The Proposed Learning Scheme and Classifiers -- Notations -- The Adaptive Incremental Learning Strategy -- The Fitness Function -- Z-value Measure -- Experimental Results -- Conclusions -- References -- SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning -- Introduction -- Naïve Bayes and Accuracy Estimation -- Motivating Example -- Selective Neighborhood Based Naïve Bayes -- Experimental Results -- Error-Rate Comparison. , Computational Requirements -- Related Work -- Conclusion -- References -- A Method to Boost Naïve Bayesian Classifiers -- Introduction -- Boosting and Naïve Bayesian Classifier -- Improved Boosting Naïve Bayesian Learning -- Algorithm Description -- Experimental Results -- Conclusion -- References -- Toward Bayesian Classifiers with Accurate Probabilities -- Introduction -- Review of Learning Simple Bayesian Networks -- Learning TAN with Accurate Probability Estimates -- Error Rate Vs AUC -- CI-based TAN Learning Algorithms -- Error-Based TAN Learning Algorithms -- AUC-based SuperParent Algorithm -- Empirical Comparisons -- Conclusion -- References -- Pruning Redundant Association Rules Using Maximum Entropy Principle -- Introduction -- Interestingness of a Rule with Respect to a Set of Constraints -- Pruning Redundant Association Rules -- Experimental Evaluation of the Pruning Algorithm -- Conclusions and Further Research -- Acknowledgments -- References -- A Confidence-Lift Support Specification for Interesting Associations Mining -- Introduction -- Problem of Support-Confidence Framework and Related Work -- Interesting Association Rules and Support Specification -- Interesting Association Rules -- The Confidence-Lift Based Support Specification -- Methods for Mining Interesting Associations Using CLS -- Experiments -- Conclusions -- References -- Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators -- Introduction -- Basic Notions and Properties -- Itemsets, Frequent Itemsets -- Closures, Closed Itemsets, and Generators -- Disjunctive Rules and Disjunction-Free Sets -- Overview of Concise Lossless Representations -- Closed Itemsets Representation -- Generators Representation -- Disjunction-Free Sets Representation -- Disjunction-Free Generators Representation. , New Representation of Frequent Itemsets Based on Generalized Disjunction-Free Generators -- Generalized Disjunction-Free Sets -- Generalized Disjunction-Free Generators Representation -- Algorithm for Computing New Representation -- Experimental Results -- Conclusions -- References -- Mining Interesting Association Rules: A Data Mining Language -- Introduction -- Data Mining Language and Database Transformation -- Mining Interesting Association Rules -- Query Processing for Type I Query -- Query Processing for Type II Query -- References -- The Lorenz Dominance Order as a Measure of Interestingness in KDD -- Introduction -- Theoretical Results -- Conclusion -- References -- Efficient Algorithms for Incremental Update of Frequent Sequences -- Introduction -- Model -- Related Works -- Algorithms -- texttt {GSP+} -- texttt {MFS+} -- Results -- Performance -- Varying |Delta ^+| and |Delta ^-| -- Conclusions -- References -- DELISP: Efficient Discovery of Generalized Sequential Patterns by Delimited Pattern-Growth Technology -- Introduction -- Problem Statement -- DELISP: Delimited Sequential Pattern Mining -- Mining Generalized Sequential Patterns by DELISP: An Example -- The DELISP Algorithm -- Experimental Results -- Discussion -- Conclusion -- Acknowledgements -- References -- Self-Similarity for Data Mining and Predictive Modeling - A Case Study for Network Data -- Introduction -- Related Work and Background -- Self-Similar Layered Hidden Markov Model (SSLHMM) -- Experimental Result -- Acknowledgement -- References -- A New Mechanism of Mining Network Behavior -- Introduction -- Our Method -- Preprocessing Phase -- Renumber Sort Algorithm -- Preprocessing Phase -- Two-Layer Pattern Discovering Phase (2LPD) -- Behavior Clustering Stage -- UserŁs Sequence Transforming Stage -- Sequential Pattern Mining Stage -- Pattern Explanation Phase. , Conclusions -- References -- M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining -- Introduction -- Visual Cluster Validation with FastMap -- M-FastMap Algorithm -- Experiments -- Experimental Results -- Conclusions -- References -- An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory -- Section 1. Introduction -- Section 2. The GRIN Algorithm -- Section 3. The Gravity-Based Hierarchical Clustering Algorithm -- Section 4. Experiments -- Section 5. Conclusions -- References -- Adding Personality to Information Clustering -- Introduction -- System Architecture -- Algorithms -- Clustering -- Personalization -- Experiments -- References -- Clustering Large Categorical Data -- Introduction -- k-modes Algorithm -- Mixture Model and Classification Likelihood Approach -- General Context -- Categorical Data -- k-modes Criterion -- Simulated Data Sets -- Summary -- References -- WebFrame: In Pursuit of Computationally and Cognitively Efficient Web Mining -- Introduction -- System Architecture for Web Mining Framework -- An Architecture Instance: Creating NCMs -- Visualization -- Experiments with Navigation Compression Models -- Data Preparation -- Evaluation of Usage Improvement -- Experimental Results -- Visualization of Navigation Compression Modules -- Summary and Conclusions -- References -- Naviz : Website Navigational Behavior Visualizer -- Introduction -- Related Works -- Naviz: Navigational Behavior Visualizer -- Known Problems in Previous Works -- Traversal Diagram -- System Overview -- Features -- Visualization of NTT i-Townpage Served on i-Mode -- Conclusion and Future Works -- Acknowledgment -- References -- Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs -- Introduction -- Web Logs and User Access Sessions -- Data Structures and Algorithms. , Theoretical Analysis.