[Computational-biology] SDM'09: The 9th SIAM international conference on data mining --- call for papers

Aristides Gionis via comp-bio%40net.bio.net (by gionis from yahoo-inc.com)
Tue Jun 10 13:26:11 EST 2008


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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



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