Newsgroups: comp.parallel From: Michael Beddows Subject: ANNOUNCEMENT: Data Mining and Knowledge Discovery journal Organization: GTE Laboratories Inc. Date: 6 Jan 1997 13:11:42 GMT Message-ID: <5aqtme$p0t@server1.ctc.com> Data Mining and Knowledge Discovery journal: Premiere Issue -- free copies available ! Call For Papers http://www.research.microsoft.com/research/datamine Below are the contents of the first issue of the new journal: Knowledge Discovery and Data Mining, Kluwer Academic Publishers. The journal is accepting submissions of works from a wide variety of fields that relate to data mining and knowledge discovery in databases (KDD). We accept regular research contributions, survey articles, application details papers, as well as short (2-page) application summaries. The goal is for Data Mining and Knowledge Discovery to become the premiere forum for publishing high quality original work from the wide variety of fields on which KDD draws, including: statistics, pattern recognition, database research and systems, modelling uncertainty and decision making, neural networks, machine learning, OLAP, data warehousing, high-performance and parallel computing, and visualization. The goal is to create a reference resource where researchers and practitioners in the area can lookup and communicate relevant work from a wide variety of fields. The journal's homepage provides detailed call for papers, description of the journal and its scope, and a list of the Editorial Board. Abstracts of the articles in the firstissue and the editorial are also on-line. The home page is maintained at: http://www.research.microsoft.com/research/datamine - If you are interested in submitting a paper, please visit the homepage: http://www.research.microsoft.com/research/datamine to look up instructions. - if you would like a free sample issue sent to you, click on the link in http://www.research.microsoft.com/research/datamine and provide the address via the on-line form. Usama Fayyad, co-Editor-in-Chief Data Mining and Knowledge Discovery (datamine@microsoft.com) ======================================================================= Data Mining and Knowledge Discovery http://www.research.microsoft.com/research/datamine CONTENTS OF: Volume 1, Issue 1 ============================== For more details, abstracts, and on-line version of Editorial, see http://www.research.microsoft.com/research/datamine/vol1-1 ===========Volume 1, Number 1, March 1997=========== EDITORIAL by Usama Fayyad PAPERS ====== Statistical Themes and Lessons for Data Mining Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-Tab, and Sub Totals Jim Gray, Surajit Chaudhuri, Adam Bosworth, Andrew Layman, Don Reichart, Murali Venkatrao, Frank Pellow, IBM, Toronto, Hamid Pirahesh On Bias, Variance, 0/1 - loss, and the Curse-of-Dimensionality Jerome H. Friedman Bayesian Networks for Data Mining David Heckerman BRIEF APPLICATIONS SUMMARIES: ============================ Advanced Scout: Data Mining and Knowledge Discovery in NBA data Ed Colet, Inderpal Bhandari, Jennifer Parker, Zachary Pines, Rajiv Pratap, Krishnakumar Ramanujam ------------------------------------------------------------------------ To get a free sample copy of the above issue, visit the web page at http://www.research.microsoft.com/research/datamine Those who do not have web access may send their address to Kluwer by e-mail at: sdelman@wkap.com -- Articles to bigrigg+parallel@pitt.edu (Administrative: bigrigg@cs.pitt.edu) Archive: http://www.hensa.ac.uk/parallel/internet/usenet/comp.parallel