CFP

Call for Papers: Special Issue on Data Mining in Science
IEEE Computing in Science & Engineering (CiSE)
Guest Editor: George Karypis
http://www.cs.umn.edu/~karypis/special-issue
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Data mining is the process of automatically extracting new and useful
knowledge hidden in large datasets. This emerging discipline is becoming
increasingly important as advances in data collection have lead to the
explosive growth in the amount of available data. To date, the majority
of the research has been focused on developing algorithms for datasets
arising in various business, information retrieval, and financial
applications. However, the success of data mining techniques in these
fields has sparked an interest of applying such analysis techniques to
various scientific and engineering fields, such as biology, medicine,
fluid dynamics, astronomy, ecosystem modeling, and structural mechanics.

The purpose of this special issue is to report both on innovative new
data mining algorithms suited for analyzing scientific datasets and to
present novel applications and success stories from analyzing scientific
data sets using data mining techniques.

Submission Instructions
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Authors are encouraged to submit high quality, original work that has
neither appeared in, nor is under consideration by other journals.
Papers should be at most 15 pages long, containing 5,000-7,000 words
(figures and tables count as 350 words each). References should be
limited to 12 citations.

Papers in PDF/PS format can be sent to karypis@cs.umn.edu by email.
If electronic submission is not possible, please send five hard copies
to the following address:

George Karypis
Department of Computer Science & Engineering
4-192 EE/CS
200 Union St, SE
Minneapolis, MN 55455, USA.

Important Dates:
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24th February, 2002       Final deadline for submission of papers
20th April, 2002          Notification of acceptance/rejection
15th May, 2002            Deadline for having final papers at the publisher
July/August 2002          Publication of the special issue

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George Karypis email: karypis@cs.umn.edu
Assistant Professor,          tel: 612-626-7524; fax: 612-625-0572
University of Minnesota, URL: http://www.cs.umn.edu/~karypis
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