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Data Mining and Knowledge Discovery
``` Date: Thu, 28 Dec 1995 11:14:01 -0800 From: etzioni@cs.washington.edu (Oren Etzioni) Subject: [fayyad@aig.jpl.nasa.gov: ASCII CFP - JDMKD]
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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@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:
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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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