-------- Original-Nachricht -------- Betreff: [computational.science] SDM'09: THE NINTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING Datum: Thu, 12 Jun 2008 19:07:02 -0400 Von: Wei Wang weiwang@cs.unc.edu Organisation: "OptimaNumerics" An: Computational Science Mailing List computational.science@lists.optimanumerics.com
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SDM'09: THE NINTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING
John Ascuaga's Nugget, Sparks (Reno-Sparks-Tahoe area), Nevada, USA April 30 - May 2, 2009
URL: http://www.siam.org/meetings/sdm09
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Important Dates:
Abstract Due: October 3, 2008 Manuscript Due: October 10, 2008 Author Notification: December 15, 2008 Camera Ready: January 26, 2009
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Data mining is an important tool in science, engineering, industrial processes, healthcare, business, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high-performance and principled analysis techniques and algorithms, based on sound theoretical and statistical foundations. These techniques in turn require powerful visualization technologies; implementations that must be carefully tuned for performance; software systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them.
This conference provides a venue for researchers who are addressing these problems to present their work in a peer-reviewed forum. It also provides an ideal setting for graduate students and others new to the field to learn about cutting-edge research by hearing outstanding invited speakers and attending tutorials (included with conference registration). A set of focused workshops are also held on the last day of the conference. The proceedings of the conference are published in archival form, and are also made available on the SIAM web site.
=================Topics of Interest==========================
Methods and Algorithms: Classification Clustering Frequent Pattern Mining Probabilistic and Statistical Methods Spatial and Temporal Mining Data Stream Mining Abnormality and Outlier Detection Feature Selection / Feature Extraction Dimension Reduction Data Reduction Mining with Constraints Data Cleaning and Noise Reduction Computational Learning Theory Multi-Task Learning Adaptive Algorithms Scalable and High-Performance Mining Mining Graphs Mining Semistructured Data Mining Complex Datasets Mining on Emerging Architectures Text and Web Mining Other Novel Methods
Applications: Astronomy & Astrophysics High Energy Physics Collaborative Filtering Earth Science Risk Management Supply Chain Management Customer Relationship Management Finance Genomics and Bioinformatics Drug Discovery Healthcare Management Automation & Process Control Logistics Management Intrusion and Fraud detection Bio-surveillance Sensor Network Applications Social Network Analysis Intelligence Analysis Other Novel Applications and Case Studies
Human Factors and Social Issues: Ethics of Data Mining Intellectual Ownership Privacy Models Privacy Preserving Data Mining and Data Publishing Risk Analysis User Interfaces Interestingness and Relevance Data and Result Visualization
=====================Organizing Committee =====================
STEERING COMMITTEE CHAIR Chandrika Kamath, Lawrence Livermore National Laboratory
GENERAL CHAIRS Haesun Park, Georgia Institute of Technology Srinivasan Parthasarathy, The Ohio State University
PROGRAM CHAIRS Huan Liu, Arizona State University Zoran Obradovic, Temple University
WORKSHOP CHAIRS Ian Davidson, University of California, Davis Carlotta Domeniconi, George Mason University
TUTORIAL CHAIR Bart Goethals, University of Antwerp
PUBLICITY CHAIRS Aristides Gionis, Yahoo! Research, Barcelona Lim Ee Peng, Nanyang Technological University, Singapore Wei Wang, University of North Carolina
SPONSORSHIP CHAIRS Wei Fan, IBM T. J. Watson Research Center Vasant Honavar, Iowa State University
PUBLICATIONS CHAIR Pang-Ning Tan, Michigan State University