Call for Papers
KDD-99: The ACM SIGKDD Fifth International Conference
on Knowledge Discovery and Data Mining
August 15-18, 1999, San Diego, CA, USA
http://research.microsoft.com/datamine/kdd99/
Sponsored by:
Association for Computing Machinery (ACM) - SIGKDD
Co-sponsored by:
AAAI, ACM SIGMOD, and ACM SIGART
The continuing rapid growth of on-line data and the widespread use of
databases necessitate the development of techniques for extracting
useful knowledge and for facilitating database access. The challenge
of extracting knowledge from data is of common interest to several
fields, including statistics, databases, pattern recognition, machine
learning, data visualization, optimization, and high-performance
computing. KDD-99 will focus on techniques, applications, and
experiences, bringing together researchers and practitioners.
Starting this year, the KDD series will represent the annual
conferences of the newly formed SIGKDD--the ACM Special Interest Group
on Knowledge Discovery and Data Mining.
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Calendar
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Electronic abstracts due: March 1, 1999
Submissions due: March 5, 1999
Notification of acceptance/rejection: May 17, 1999
Camera-ready copies due: June 14, 1999
Submission Guidelines: Please see the KDD-99 web site for detailed
instructions (http://research.microsoft.com/datamine/KDD99). All
submissions (including research papers, panels, demos, tutorials and
industrial track submissions) must be received by March 5, 1998.
Prospective authors are encouraged to submit research papers on any
topics of relevance to knowledge discovery and data mining. In
addition to fundamental research, we solicit papers fostering
cross-fertilization and interdisciplinary integration, as well as
papers that describe significant experiences and implementation
lessons. Topics of interest include, but are not limited to:
KDD Techniques Human Interaction and the KDD Process
--- ---------- ----- ----------- --- --- --- -------
New KDD algorithms Data and knowledge visualization
Mining the Web Evaluating knowledge and potential discoveries
Text/multimedia Interactive exploration
Data cleaning/noisy data Visualizing large, high-dimensional data
Incremental algorithms
High-dimensional data
Background knowledge Mining Enterprise Databases
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Implementation and Applications Scalable algorithms
-------------- --- ------------ Unification of mining with querying
Implementation & use of KDD systems Database architectures for KDD
Vertical applications Database primitives for KDD
Case studies: success/failure Integration: mining/warehousing/OLAP
Benchmarks
KDD-99 Organization
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Program Committee Co-Chairs: Surajit Chaudhuri (SurajitC@microsoft.com)
David Madigan (madigan@stat.washington.edu)
General Chair: Usama Fayyad (Fayyad@microsoft.com)
Awards: G. Piatetsky-Shapiro (gps@kstream.com)
Demos/Exhibits: Ismail Parsa (iparsa@epsilon.com)
Industrial/Applications Track: Jim Gray (gray@microsoft.com)
Ronny Kohavi (ronnyk@bluemartini.com)
Local Arrangements: Jenny Zhang (jgz@hnc.com)
Panels: Padhraic Smyth (smyth@ics.uci.edu)
Proceedings: Kyuseok Shim (shim@research.bell-labs.com)
Publicity: Foster Provost (provost@acm.org)
Sponsorship: Ramasamy Uthurusamy (samy@iss.gm.com)
Tutorials: Jiawei Han (han@cs.sfu.ca)
Workshops: Rakesh Agrawal (ragrawal@almaden.ibm.com)
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