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Topic: ANNOUNCEMENT: Data Mining and Knowledge Discovery journal
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Michael Beddows

Posts: 2
Registered: 12/15/04
ANNOUNCEMENT: Data Mining and Knowledge Discovery journal
Posted: Dec 6, 1996 6:54 PM
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Data Mining and Knowledge Discovery journal:
Premiere Issue -- free copies available !
Call For Papers

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:

- If you are interested in submitting a paper, please visit the
to look up instructions.

- if you would like a free sample issue sent to you, click on
the link in
and provide the address via the on-line form.
Usama Fayyad, co-Editor-in-Chief
Data Mining and Knowledge Discovery (


Data Mining and Knowledge Discovery

CONTENTS OF: Volume 1, Issue 1
For more details, abstracts, and on-line version of Editorial, see

===========Volume 1, Number 1, March 1997===========

EDITORIAL by Usama Fayyad


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


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
Those who do not have web access may send their address to Kluwer
by e-mail at:

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