Data Mining and Knowledge Discovery (fwd)
Paula Davidson
davidson at cs.unca.edu
Fri Dec 29 18:08:37 EST 1995
Very interesting to information retrieval, eh?
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Paula Davidson
mesoelectronic hunter/gatherer
davidson at cs.unca.edu http://www.cs.unca.edu/~davidson/
Specializing in Exploration and Tool Use on the Matrix of the Net
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---------- Forwarded message ----------
Date: Thu, 28 Dec 1995 18:02:08 -0800 (PST)
From: Phil Agre <pagre at weber.ucsd.edu>
To: rre at weber.ucsd.edu
Subject: Data Mining and Knowledge Discovery
Date: Thu, 28 Dec 1995 11:14:01 -0800
From: etzioni at cs.washington.edu (Oren Etzioni)
Subject: [fayyad at aig.jpl.nasa.gov: ASCII CFP - JDMKD]
****************************************************************
New Journal Announcement:
Data Mining and Knowledge Discovery
C a l l f o r P a p e r s
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Advances in data gathering, storage, and distribution technologies have far
outpaced computational advances in techniques for analyzing and understanding
data. This created an urgent need for a new generation of tools and
techniques for automated Data Mining and Knowledge Discovery in Databases
(KDD). KDD is a broad area that integrates methods from several fields
including machine learning, machine discovery, uncertainty modeling,
statistics, databases, data visualization, high performance computing,
management information systems (MIS), and knowledge-based systems.
KDD refers to a multi-step process that can be highly interactive and
iterative and which includes data selection, preprocessing, transformation,
application of data mining algorithms to extract patterns/models from data,
evaluating the extracted patterns, and converting them to an operational form
or human-oriented knowledge. Hence "data mining" refers to a step in the
overall KDD process. However, a significant portion of the published work
has focused on the development and application of data mining methods for
pattern/model esxtraction from data using automated or semi-automated
techniques. Hence, by including it explicitly in the name of the journal, we
hope to emphasize its role, and build bridges to communities working solely
on data mining methods.
Our goal is to make the journal of Data Mining and Knowledge Discovery a
flagship publication in the KDD area, providing a unified forum for the KDD
research community, whose publications are currently scattered among many
different journals. The journal will publish state-of-the-art papers in both
the research and practice of KDD, surveys of important techniques from
related fields, and application papers of general interest. In addition,
there will be a section for publishing useful information such as short
application reports (1-3 pages), book and system reviews, and relevant
product announcements.
The topics of interest include:
Theory and Foundational Issues in KDD:
Data and knowledge representation for KDD
Modeling of structured, textual, and multimedia data
Uncertainty management in KDD
Metrics for evaluating interestingness and utility of knowledge
Algorithmic complexity, efficiency, and scalability issues in data mining
Limitations of data mining methods
Data Mining Methods and Algorithms:
Discovery methods based on belief networks, decision trees,
genetic programming, neural networks, rough sets, and other approaches
Algorithms for mining spatial, textual, and other complex data
Incremental discovery methods and re-use of discovered knowledge
Integration of discovery methods
Data structures and query evaluation methods for data mining
Parallel and distributed data mining techniques
Issues and challenges for dealing with massive or small data sets
Knowledge Discovery Process
Data pre-processing for data mining
Evaluating, consolidating, and explaining discovered knowledge
Data and knowledge visualization
Interactive data exploration and discovery
Application Issues:
Application case studies
Data mining systems and tools
Details of successes and failures of KDD
Resource and knowledge discovery on the Internet and WWW
Privacy and security issues
This list of topics is not intended to be exhaustive but an indication of
typical topics of interest. Prospective authors are encouraged to submit
papers on any topics of relevance to knowledge discovery and data mining.
SUBMISSION AND REVIEW CRITERIA: We solicit papers on both research and
applications. All submitted papers should be relevant to KDD, clearly
written, and be accessible to readers from other disciplines by including a
carefully written introduction. Submissions will be thouroughly reviewed to
ensure they make a substantial advance either in increasing our understanding
of a fundamental theoretical problem, or provide a strong technological
advance enabling the algorithmic extraction of knowledge from data. Papers
whose primary focus is on significant applications are strongly encouraged
but must clearly address the general underlying issues and principles, as
well as provide details of algorithmic aspects. Papers whose primary focus
is on algorithms and methods must address issues of complexity,
efficiency/feasibility for large data sets, and clearly state assumptions and
limitations of methods covered. Short application summaries (1-3 pages) are
also encouraged and would be judged on the basis of application significance,
technical innovation, and clarity of presentation.
SUBMISSION INSTRUCTIONS:
We encourage electronic submission of postscript files.
Authors should submit five hard copies of their manuscript to:
Ms. Karen Cullen , DATA MINING AND KNOWLEDGE DISCOVERY
Editorial Office, Kluwer Academic Publishers,
101 Philip Drive, Norwell, MA 02061
phone 617-871-6600 fax 617-871-6528 email: kcullen at wkap.com
Submissions should be in 12pt font, 1.5 line-spacing, and should not
exceed 28 pages. We strongly encourage electronic submissions, please
visit http://www.research.microsoft.com/research/datamine/ to obtain
instructions on electronic submissions. Detailed instructions for
submission of final manuscripts and Kluwer format files for LaTex, MS Word,
and other typestting programs are provided at the above site.
Exact instructions for hardcopy and electronic submission to Kluwer
can be accessed at http://www.research.microsoft.com/research/datamine/
Being a publication for a rapidly emerging field, the journal would emphasize
quick dissemination of results and minimal backlogs in publication time. We
plan to review papers and respond to authors within 3 months of submission.
An electronic server will be made available by Kluwer for access to accepted
papers by all subscribers to the journal. Authors would be encouraged to
make their data available via the journal web site by allowing papers to
have an "electronic appendix", containing data and/or algorithms authors
may want to publish when appropriate.
The journal will be a quarterly, with a first volume published in January 1997
by Kluwer Academic Publishers.
Editors-in-Chief: Usama M. Fayyad
================ Jet Propulsion Laboratory,
California Institute of Technology, USA
Heikki Mannila
University of Helsinki, Finland
Gregory Piatetsky-Shapiro
GTE Laboratories, USA
Editorial Board:
===============
Rakesh Agrawal (IBM Almaden Research Center, USA)
Tej Anand (AT&T Global Information Solutions, USA)
Ron Brachman (AT&T Bell Laboratories, USA)
Wray Buntine (Heuristicrats Research Inc, USA)
Peter Cheeseman (NASA AMES Research Center, USA)
Greg Cooper (University of Pittsburgh, USA)
Bruce Croft (University of Mass. Amherst, USA)
Dan Druker (Arbor Software, USA)
Saso Dzeroski (Josef Stefan Institute, Slovenia)
Oren Etzioni (University of Washington, USA)
Jerome Friedman (Stanford University, USA)
Brian Gaines (University of Calgary, Canada)
Clark Glymour (Carnegie-Mellon University, USA)
Jim Gray (Microsoft Research, USA)
Georges Grinstein (University of Lowell, USA)
Jiawei Han (Simon Fraser University, Canada)
David Hand (Open University, UK)
Trevor Hastie (Stanford University, USA)
David Heckerman (Microsoft Research, USA)
Se June Hong (IBM T.J. Watson Research Center, USA)
Thomasz Imielinski (Rutgers University, USA)
Larry Jackel (AT&T Bell Labs, USA)
Larry Kerschberg (George Mason University, USA)
Willi Kloesgen (GMD, Germany)
Yves Kodratoff (Lab. de Recherche Informatique, France)
Pat Langley (ISLE/Stanford University, USA)
Tsau Lin (San Jose State University, USA)
David Madigan (University of Washington, USA)
Ami Motro (George Mason University, USA)
Shojiro Nishio (Osaka University, Japan)
Judea Pearl (University of California, Los Angeles, USA)
Ed Pednault (AT&T Bell Labs, USA)
Daryl Pregibon (AT&T Bell Laboratories, USA)
J. Ross Quinlan (University of Sydney, Australia)
Jude Shavlik (University of Wisconsin - Madison, USA)
Arno Siebes (CWI, Netherlands)
Evangelos Simoudis (IBM Almaden Research Center, USA)
Andrzej Skowron (University of Warsaw, Poland)
Padhraic Smyth (Jet Propulsion Laboratory, USA)
Salvatore Stolfo (Columbia University, USA)
Alex Tuzhilin (NYU Stern School, USA)
Ramasamy Uthurusamy (General Motors Research Laboratories, USA)
Vladimir Vapnik (AT&T Bell Labs, USA)
Ronald Yager (Iona College, USA)
Xindong Wu (Monash University, Australia)
Wojciech Ziarko (University of Regina, Canada)
Jan Zytkow (Wichita State University, USA)
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