Abstracts due Feb 11 for 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2011), Aug 21-24, 2011, in San Diego, CA http://www.kdd.org/kdd2011/
KDD-2011
17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 21-24, 2011, Manchester Grand Hyatt San Diego, CA
www.kdd.org/kdd2011/
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Facebook (www.facebook.com/event.php?eid=162069847153393) and LinkedIn (coming soon)
New for KDD 2011:
Industry Practice Expo: www.kdd.org/kdd2011/indexpo.shtml
Key Dates:
- Aug 21-24, 2011 KDD-2011 Conference
- June 30, 2011 KDD Cup Competition Ends
- May 13, 2011 Paper Acceptance
- Mar 15, 2011 KDD Cup Competition Begins
- Mar 1, 2011 KDD Cup Registration Opens (www.kdd.org/kdd2011/kddcup.shtml)
- Mar 1, 2011 Notification of Workshop Decisions
Paper submission and reviewing will be handled electronically. Authors should consult the conference Web site for full details regarding paper preparation and submission guidelines. Papers submitted to KDD 2011 should be original work and substantively different from papers that have been previously published or are under review in a journal or another peer-reviewed conference.
RESEARCH TRACK:
We invite submission of papers describing innovative research on all
aspects of knowledge discovery and data mining. Examples of topic of
interest include (but are not limited to): classification and
regression methods, semi-supervised learning, clustering, feature
selection, social networks, mining of graph data, temporal and spatial
data analysis, scalability, privacy, visualization, text analysis, Web
mining, recommender systems, and so on. Papers emphasizing theoretical
foundations are particularly encouraged, as are novel modeling and
algorithmic approaches to specific data mining problems in scientific,
business, medical, and engineering applications. We welcome
submissions by authors who are new to the KDD conference, as well as
visionary papers on new and emerging topics. Authors are explicitly
discouraged from submitting papers that contain only incremental
results and that do not provide significant advances over existing
approaches.
Submitted papers will be assessed based on their novelty, technical
quality, potential impact, and clarity of writing. For papers that
rely heavily on empirical evaluations, the experimental methods and
results should be clear, well executed, and repeatable. Authors are
strongly encouraged to make data and code publicly available when
possible.
INDUSTRIAL TRACK:
The Industrial/Government Applications Track solicits papers
describing implementations of KDD solutions relevant to industrial or
government settings. The primary emphasis is on papers that advance
the understanding of practical, applied, or pragmatic issues related
to the use of KDD technologies in industry and government and
highlight new research challenges arising from attempts to create such
real KDD applications. Applications can be in any field including, but
not limited to: e-commerce, medical and pharmaceutical, defense,
public policy, engineering, manufacturing, telecommunications, and
government.
The Industrial/Government Applications Track will consist of
competitively-selected contributed papers. Submitters must clearly
identify in which of the following three sub-areas their paper should
be evaluated as distinct review criteria will be used to evaluate each
category of submission.
Deployed KDD systems that are providing real value to industry,
Government, or other organizations or professions. These deployed
systems could support ongoing knowledge discovery or could be
applications that employ discovered knowledge, or some combination of
the two.
Discoveries of knowledge with demonstrable value to Industry,
Government, or other users (e.g., scientific or medical professions).
This knowledge must be "externally validated" as interesting and
useful; it can not simply be a model that has better performance on
some traditional KDD metric such as accuracy or area under the curve.
Emerging applications and technology that provide insight relevant to
the above value propositions. These emerging applications must have
clear user interest and support to distinguish them from KDD research
papers, or they must provide insight into issues and factors that
affect the successful use of KDD technology and methods. Papers that
describe infrastructure that enables the large-scale deployment of KDD
techniques also are in this area.
ON BEHALF OF THE KDD-2011 ORGANIZERS:
General Chair: Chid Apte (IBM Research)
Research Program Co-chairs: Joydeep Ghosh (University of Texas, Austin) Padhraic Smyth (University of California, Irvine)
Industry and Government Program Co-chairs: Ted Senator (SAIC) Michael Zeller (Zementis)
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