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ORCID2nd SDM 2002: Arlington, VA, USA
- Robert L. Grossman, Jiawei Han, Vipin Kumar, Heikki Mannila, Rajeev Motwani:
Proceedings of the Second SIAM International Conference on Data Mining, Arlington, VA, USA, April 11-13, 2002. SIAM 2002, ISBN 978-0-89871-517-0
Part I: Visualization and Applications
- Ian Davidson:
Visualizing Clustering Results. 3-18 - 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. 19-40 - Wei Fan, Salvatore J. Stolfo:
Ensemble-based Adaptive Intrusion Detection. 41-58 - Kai Yu, Xiaowei Xu, Jianjua Tao, Martin Ester, Hans-Peter Kriegel:
Instance Selection Techniques for Memory-based Collaborative Filtering. 59-74
Part II: Mining Large Data Sets
- Ruoming Jin, Gagan Agrawal:
Shared Memory Paraellization of Data Mining Algorithms: Techniques, Programming Interface, and Performance. 77-94 - Arindam Choudhury, Prasanth B. Nair, Andy J. Keane:
A Data Parallel Approach for Large-Scale Gaussian Process Modeling. 95-111 - Yi Xia, Wei Wang, Jiong Yang, Philip S. Yu, Richard R. Muntz:
Efficient Filtering of Large DatasetA User-Centric Paradigm. 112-127 - 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. 128-138
Part III: Mining Sequential and Structured Patterns
- Cheng-Ru Lin, Ming-Syan Chen:
On the Optimal Clustering of Sequential Data. 141-157 - Tatsuya Asai, Kenji Abe, Shinji Kawasoe, Hiroki Arimura, Hiroshi Sakamoto, Setsuo Arikawa:
Efficient Substructure Discovery from Large Semi-structured Data. 158-174 - Gao Cong, Lan Yi, Bing Liu, Ke Wang:
Discovering Frequent Substructures from Hierarchical Semi-structured Data. 175-192
Part IV: Time Series Analysis
- Selina Chu, Eamonn J. Keogh, David M. Hart, Michael J. Pazzani:
Iterative Deepening Dynamic Time Warping for Time Series. 195-212 - João B. D. Cabrera, Raman K. Mehra:
Extracting Precursor Rules from Time SeriesA Classical Statistical Viewpoint. 213-228 - Christopher Meek, David Maxwell Chickering, David Heckerman:
Autoregressive Tree Models for Time-Series Analysis. 229-244
Part V: Support Vector Machine and Neural Networks
- Glenn Fung, Olvi L. Mangasarian:
Incremental Support Vector Machine Classification. 247-260 - Michinari Momma, Kristin P. Bennett:
A Pattern Search Method for Model Selection of Support Vector Regression. 261-274 - Olivier Adam, Olivier Léonard:
Explicit Thermodynamic Properties using Radial Basis Functions Neural Networks. 275-296
Part VI: Clustering
- Sergio M. Savaresi, Daniel Boley, Sergio Bittanti, Giovanna Gazzaniga:
Cluster Selection in Divisive Clustering Algorithms. 299-314 - Hasan Davulcu, Saikat Mukherjee, I. V. Ramakrishnan:
A Clustering Technique for Mining Data from Text Tables. 315-332 - Arindam Banerjee, Joydeep Ghosh:
On Scaling Up Balanced Clustering Algorithms. 333-349
Part VII: Classification and Decision Tables
- Carlotta Domeniconi, Dimitrios Gunopulos:
Efficient Local Flexible Nearest Neighbor Classification. 353-369 - Chandrika Kamath, Erick Cantú-Paz, David Littau:
Approximate Splitting for Ensembles of Trees using Histograms. 370-383 - Rattikorn Hewett, John H. Leuchner:
The Power of Second-Order Decision Tables. 384-399
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. 403-419 - Pavel Berkhin, Jonathan D. Becher:
Learning Simple Relations: Theory and Applications. 420-436 - Wei Fan, Haixun Wang, Philip S. Yu, Salvatore J. Stolfo:
A Framework for Scalable Cost-sensitive Learning Based on Combing Probabilities and Benefits. 437-453
Part IX: Mining Frequent Patterns
- Mohammed Javeed Zaki, Ching-Jiu Hsiao:
CHARM: An Efficient Algorithm for Closed Itemset Mining. 457-473 - Vasileios Megalooikonomou:
Evaluating the Performance of Association Mining Methods in 3-D Medical Image Databases. 474-493 - Adriano Veloso, Wagner Meira Jr., Márcio de Carvalho, Bruno Pôssas, Srinivasan Parthasarathy, Mohammed Javeed Zaki:
Mining Frequent Itemsets in Evolving Databases. 494-510 - Feng Liang, Sheng Ma, Joseph L. Hellerstein:
Discovering Fully Dependent Patterns. 511-527
Part X: Applications
- Olfa Nasraoui, Raghu Krishnapuram:
One Step Evolutionary Mining of Context Sensitive Associations and Web Navigation Patterns. 531-547 - Pankaj Kankar, Sudeshna Adak, A. Sarkar, K. Murali, Gaurav Sharma:
MedMeSH Summarizer: Text Mining for Gene Clusters. 548-565 - Ramesh Natarajan, Edwin P. D. Pednault:
Segmented Regression Estimators for Massive Data Sets. 566-582 - 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. 583-597
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