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.

last updated on Thu May 23 03:41:34 2013 by the dblp team
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