2. SDM 2002: Arlington, VA, USA

Part I: Visualization and Applications

• Ian Davidson:
  Visualizing Clustering Results.
  
• Li Zhang, Chun Tang, Yong Shi, Yuqing Song, Aidong Zhang, Murali Ramanathan:
  VizCluster: An Interactive Visualization Approach to Cluster Analysis and Its Application on Microarray Data.
  
• Wei Fan, Salvatore J. Stolfo:
  Ensemble-based Adaptive Intrusion Detection.
  
• Kai Yu, Xiaowei Xu, Jianjua Tao, Martin Ester, Hans-Peter Kriegel:
  Instance Selection Techniques for Memory-based Collaborative Filtering.
  

Part II: Mining Large Data Sets

• Ruoming Jin, Gagan Agrawal:
  Shared Memory Paraellization of Data Mining Algorithms: Techniques, Programming Interface, and Performance.
  
• Arindam Choudhury, Prasanth B. Nair, Andy J. Keane:
  A Data Parallel Approach for Large-Scale Gaussian Process Modeling.
  
• Yi Xia, Wei Wang, Jiong Yang, Philip S. Yu, Richard R. Muntz:
  Efficient Filtering of Large DatasetA User-Centric Paradigm.
  
• Peter van der Putten, Joost N. Kok, Amar Gupta:
  Why the Information Explosion Can Be Bad for Data Mining, and How Data Fusion Provides a Way Out.
  

Part III: Mining Sequential and Structured Patterns

• Cheng-Ru Lin, Ming-Syan Chen:
  On the Optimal Clustering of Sequential Data.
  
• Tatsuya Asai, Kenji Abe, Shinji Kawasoe, Hiroki Arimura, Hiroshi Sakamoto, Setsuo Arikawa:
  Efficient Substructure Discovery from Large Semi-structured Data.
  
• Gao Cong, Lan Yi, Bing Liu, Ke Wang:
  Discovering Frequent Substructures from Hierarchical Semi-structured Data.
  

Part IV: Time Series Analysis

• Selina Chu, Eamonn J. Keogh, David Hart, Michael J. Pazzani:
  Iterative Deepening Dynamic Time Warping for Time Series.
  
• João B. D. Cabrera, Raman K. Mehra:
  Extracting Precursor Rules from Time SeriesA Classical Statistical Viewpoint.
  
• Christopher Meek, David Maxwell Chickering, David Heckerman:
  Autoregressive Tree Models for Time-Series Analysis.
  

Part V: Support Vector Machine and Neural Networks

• Glenn Fung, Olvi L. Mangasarian:
  Incremental Support Vector Machine Classification.
  
• Michinari Momma, Kristin P. Bennett:
  A Pattern Search Method for Model Selection of Support Vector Regression.
  
• Olivier Adam, Olivier Léonard:
  Explicit Thermodynamic Properties using Radial Basis Functions Neural Networks.
  

Part VI: Clustering

• Sergio M. Savaresi, Daniel Boley, Sergio Bittanti, Giovanna Gazzaniga:
  Cluster Selection in Divisive Clustering Algorithms.
  
• Hasan Davulcu, Saikat Mukherjee, I. V. Ramakrishnan:
  A Clustering Technique for Mining Data from Text Tables.
  
• Arindam Banerjee, Joydeep Ghosh:
  On Scaling Up Balanced Clustering Algorithms.
  

Part VII: Classification and Decision Tables

• Carlotta Domeniconi, Dimitrios Gunopulos:
  Efficient Local Flexible Nearest Neighbor Classification.
  
• Chandrika Kamath, Erick Cantú-Paz, David Littau:
  Approximate Splitting for Ensembles of Trees using Histograms.
  
• Rattikorn Hewett, John H. Leuchner:
  The Power of Second-Order Decision Tables.
  

Part VIII: Causality Rules and Relation Learning

• Chang-Hung Lee, Philip S. Yu, Ming-Syan Chen:
  Mining Relationship between Triggering and Consequential Events in a Short Transaction Database.
  
• Pavel Berkhin, Jonathan D. Becher:
  Learning Simple Relations: Theory and Applications.
  
• Wei Fan, Haixun Wang, Philip S. Yu, Salvatore J. Stolfo:
  A Framework for Scalable Cost-sensitive Learning Based on Combing Probabilities and Benefits.
  

Part IX: Mining Frequent Patterns

• Mohammed Javeed Zaki, Ching-Jiu Hsiao:
  CHARM: An Efficient Algorithm for Closed Itemset Mining.
  
• Vasileios Megalooikonomou:
  Evaluating the Performance of Association Mining Methods in 3-D Medical Image Databases.
  
• Adriano Veloso, Wagner Meira Jr., Márcio de Carvalho, Bruno Pôssas, Srinivasan Parthasarathy, Mohammed Javeed Zaki:
  Mining Frequent Itemsets in Evolving Databases.
  
• Feng Liang, Sheng Ma, Joseph L. Hellerstein:
  Discovering Fully Dependent Patterns.
  

Part X: Applications

• Olfa Nasraoui, Raghu Krishnapuram:
  One Step Evolutionary Mining of Context Sensitive Associations and Web Navigation Patterns.
  
• Pankaj Kankar, Sudeshna Adak, A. Sarkar, K. Murali, Gaurav Sharma:
  MedMeSH Summarizer: Text Mining for Gene Clusters.
  
• Ramesh Natarajan, Edwin P. D. Pednault:
  Segmented Regression Estimators for Massive Data Sets.
  
• Bertis B. Little, Walter L. Johnston, Ashley C. Lovell, Roderick M. Rejesus, Steve A. Steed:
  Collusion in the U. S. Crop Insurance Program: Applied Data Mining.
  

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