[Elsnet-list] [Mlnet] Last CFP: Open Source Data Mining Workshop at SIGKDD 2005

Bart Goethals bart.goethals at ua.ac.be
Thu Jun 2 13:36:39 CEST 2005

OSDM 2005: Open Source Data Mining Workshop
(on Frequent Pattern Mining Implementations)
In conjunction with ACM SIGKDD 2005

Chicago, Illinois, USA
August 21st, 2005



Scope & Objectives

Over the past decade tremendous progress has been made in data mining
methods like clustering, classification, frequent pattern mining, etc.
However, unfortunately, the advanced implementations are often not
made publicly available, and thus the results cannot be independently
verified.  This hampers rapid advances in the field. There is thus a
critical need to have open source implementations of important data
mining methods.  This workshop is the first such meeting place to
discuss open source data mining methods. Since the scope of such a
workshop can be rather broad, we focus our attention in the first year
to Frequent Pattern Mining (FPM) problems.  In subsequent years we
will focus on open source implementations for other data mining
problems like clustering, classification, outlier detection, etc.

Frequent pattern mining is a core field of research in data mining
encompassing the discovery of patterns such as itemsets, sequences,
trees, graphs, and many other structures. Varied approaches to these
problems appear in numerous papers across all data mining
conferences. Generally speaking, the problem involves the
identification of items, products, symptoms, characteristics, and so
forth, that often occur together in a given dataset. As a fundamental
operation in data mining, algorithms for FPM can be used as a building
block for other, more sophisticated data mining approaches. During the
last decade, a huge number of algorithms have been developed in order
to efficiently solve the FPM problems.

Submissions consist of source code in addition to a paper that
describes the implemented FPM algorithm (for itemset, sequence, tree
or graph mining) and provides a performance study on publicly provided
datasets. We request that the paper also provides a deep analysis of
the proposed techniques, by presenting results on the performance of
the algorithm with and without each of the used techniques or
optimizations, and if appropriate, an explanation of why the submitted
algorithm performs better than existing implementations or algorithms.
The submitted source code will become part of an Open Source Data
Mining Repository which will be widely publicised.

The workshop participants will be invited to come and discuss the
submission; there will be a heavy focus on critical evaluation, i.e., what
are the limitations, under what conditions does the algorithm work well,
why it fails in other cases, and what are the open areas. One outcome of
the workshop will be to outline the focus for research on new problems in
the field. Although there will be no performance contest, we believe that
the open source nature of the workshop encourages authors to accurately
and honestly compare their algorithms with others, and vice versa.

More detailed submission information can be found on the website of the
workshop: http://osdm.ua.ac.be/

Important dates

Submission Deadline: June 8, 2005
Notification: July 8, 2005
Camera-ready Copies: July 18, 2005
Workshop date: August 21, 2005

Workshop co-chairs

Bart Goethals, University of Antwerp, Belgium
Siegfried Nijssen, University of Leiden, The Netherlands
Mohammed J. Zaki, Rensselaer Polytechnic Institute, USA

Publication of Workshop Proceedings

To widely disseminate the workshop results, the proceedings
will be published online in the ACM Digital Library.

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