2017 SIAM International Conference on Data Mining
分類：國外活動
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ponsored by the SIAM Activity Group on Data Mining and Analytics.
This conference is held in cooperation with the American Statistical Association.
Statement on Inclusiveness
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Announcements
Doctoral Student Forum Applications for participation in the doctoral forum are due February 6, 2017. Click here for the Call for Doctoral Student Forum Participants.
Organizing Committee
Steering Committee Chair
Srinivasan Parthasarathy, Ohio State University, USA
Steering Committee
Chid Apte, IBM T.J. Watson Research Center, USA
Christos Faloutsos, Carnegie Mellon University, USA
Joydeep Ghosh, The University of Texas at Austin, USA
Jiawei Han, University of Illinois at Urbana-Champaign, USA
Chandrika Kamath, Lawrence Livermore National Laboratory, USA
Vipin Kumar, University of Minnesota, USA
Haesun Park, Georgia Institute of Technology, USA
Srinivasan Parthasarathy, Ohio State University, USA
Qiang Yang, Hong Kong University of Science and Technology, USA
Philip Yu, University of Illinois at Chicago, USA
Conference Co-Chairs
Carlotta Domeniconi, George Mason University, USA
Dimitrios Gunopulos, University of Athens, Greece
Program Co-Chairs
Nitesh Chawla, University of Notre Dame, USA
Wei Wang, University of California, Los Angeles, USA
Workshops Co-Chairs
Leman Akoglu, Carnegie Mellon University, USA
Arthur Zimek, University of Southern Denmark, Denmark
Tutorials Chair
Jilles Vreeken, Saarland University and Max Planck Institute for Informatics, Germany
Doctoral Forum Chair
Shuiwang Ji, Washington State University, USA
Sponsorship Co-Chairs
James Faghmous, Icahn School of Medicine, Mount Sinai, USA
Jiliang Tang, Yahoo Research, USA
Panels Chair
Chris Jermaine, Rice University, USA
Publicity Co-Chairs
Danai Koutra, University of Michigan, USA
Matthijs van Leeuwen, Leiden University, The Netherlands
Panagiotis Papapetrou, Stockholm University, Sweden
Awards Chair
Zoran Obradovic, Temple University, USA
Description
Data mining is the computational process for discovering valuable knowledge from data. It has enormous application in numerous fields, including science, engineering, healthcare, business, and medicine. Typical datasets in these fields are large, complex, and often noisy. Extracting knowledge from these datasets requires the use of sophisticated, high-performance, and principled analysis techniques and algorithms, which are based on sound theoretical and statistical foundations. These techniques in turn require implementations on high performance computational infrastructure that are carefully tuned for performance. Powerful visualization technologies along with effective user interfaces are also essential to make data mining tools appealing to researchers, analysts, and application developers from different disciplines.
The SDM 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 presentations and tutorials (included with conference registration). A set of focused workshops is 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.
