Sponsored 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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Organizing Committee

Steering Committee Co-Chairs
Zoran Obradovic, Temple University, USA
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
Tanya Berger-Wolf, University of Illinois, USA
Dimitrios Gunopulos, University of Athens, Greece

Program Co-Chairs
Martin Ester, Simon Fraser University, Canada
Dino Pedreschi, University of Pisa, Italy

Workshops Co-Chairs
Joao Gama, University of Porto-LIAAD, Portugal
Jing Gao, SUNY Buffalo, USA

Tutorials Chair
Yan Liu, University of Southern California, USA

Doctoral Forum Chair
Julian McAuley, University of California, San Diego, USA

Sponsorship Co-Chairs
Matteo Riondato, TwoSigma, USA
Jiliang Tang, Michigan State University, USA

Panels Chair
To Be Decided

Publicity Co-Chairs
Zhenhui Li, Pennsylvania State University, USA
Gregor Stiglic, University of Maribor, Slovenia

Awards Chair
To Be Decided



Data mining is the computational process for discovering valuable knowledge from data – the core of modern Data Science. It has enormous applications 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. 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, data scientists and application developers from different disciplines, as well as usable by stakeholders.

SDM has established itself as a leading conference in the field of data mining and provides a venue for researchers who are addressing these problems to present their work in a peer-reviewed forum. SDM emphasizes principled methods with solid mathematical foundation, is known for its high-quality and high-impact technical papers, and offers a strong workshop and tutorial program (which are included in the conference registration). The proceedings of the conference are published in archival form, and are also made available on the SIAM web site.


Funding Agency


SIAM and the Conference Organizing Committee wish to extend their thanks and appreciation to the U.S. National Science Foundation for its support of this conference.



Methods and Algorithms


Human Factors and Social Issues


Important Deadlines

October 6, 2017 11:59PM (US Pacific Time): Abstract Submission
October 6, 2017 11:59PM (US Pacific Time): Workshop Proposals
October 6, 2017 11:59PM (US Pacific Time): Tutorial Proposals
October 13, 2017 11:59PM (US Pacific Time): Paper Submission

January 26, 2018: SIAM Student Travel Award and Post-doc/Early Career Travel Award Applications

April 3, 2018: Disconnect time is midnight EDT

April 3, 2018

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