[UAI] ICML Workshop on Data Mining Lessons Learned (DMLL-2002)

From: Nada Lavrac (Nada.Lavrac@ijs.si)
Date: Sun Mar 17 2002 - 15:39:36 PST

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                    First International Workshop on
                      Data Mining Lessons Learned
                            (DMLL-2002)
      http://www.hpl.hp.com/personal/Tom_Fawcett/DMLL-workshop.html
          
           An ICML-2002 Workshop, Sydney, 8-12 July 2002
                 http://www.cse.unsw.edu.au/~icml2002/

                         FIRST CALL FOR PAPERS

    Description

    Data mining is concerned with finding interesting/valuable patterns in
    data. Many new techniques are emerging that enable analysis and
    visualization of large volumes of data of various types. What we see
    in technical papers are mostly success stories of these new
    techniques. However, a typical paper accepted to a ML or DM conference
    shows the advantages of the new method compared to other methods by
    reporting only marginal accuracy improvements on standard benchmark
    data, mostly from the UCI repository. In rude terms, one can claim
    that many of the recent ML and DM developments in classifier induction
    are simply overfitting UCI datasets.

    Journal, conference and workshop papers seldom report on failures, yet
    reported failures are crucial for increasing awareness of which
    approach to use in which situations. At conferences, we often hear of
    successful applications; but we rarely hear of steps leading to
    success, steps that failed, and representation choices that were
    critical to success. In addition, papers rarely include expert
    evaluations of results. Fortunately, recent years have seen an
    increase in popularity of data mining challenge problems, such as the
    KDD Cup, the COIL challenge and the PTE challenge. Since they pose
    specific problems to data mining researchers and provide expert
    evaluation of solutions, the can serve as a source of such experience.

    The purpose of this workshop is to assemble data mining practitioners
    and to gather experience from successful and unsuccessful data mining
    endeavors. We seek to learn from successes (evaluated by domain
    experts) and, most importantly, from failed data mining endeavors (why
    was an approach not successful, what can dataminers learn from this
    experience). The main aim is to start gathering the lessons learned.

    Goals

    The aim of this workshop is to collect the experience gained through
    different challenge competitions as well as the lessons learned from
    the failed and successful approaches to solving particular data mining
    applications. Authors are invited to report on lessons learned from
    submissions to challenge problems, experiences in engineering the
    representations to be particularly useful for the problem at hand,
    what kind of output the expert evaluating the solutions liked (e.g.,
    should it be natural language) and how intelligible the output was to
    the expert, why some particular solutions - despite their good
    predictive accuracy - never made it to practice, obtained results
    which were not originally expected before solving the problem.

    An ideal contributions to this workshop would describe one problem in
    sufficient detail, either an application or a challenge problem.
    - For applications, we expect authors to describe approaches which
    failed, why they failed, what has to be done to succeed if at all,
    what was the expert's reactions/opinion/suggestions, what the expert
    liked/disliked strongly, what are the conditions to make the system
    practically useful, what were the reasons for success and what were
    the reasons for failures.
    - For challenge problems, lessons learned from individual
    contributions and from the organization of such a challenge can be
    reported as well as the characteristics of successful approaches that
    made them appealing to experts evaluating the results.

    Contributions not desired for this workshop would report on marginal
    improvement over existing methods using artificial synthetic data or
    UCI data involving no expert evaluation.

    Workshop format

    The workshop will consist of presentations of submitted papers, and a
    panel discussion.

    If there is sufficient interest and the workshop contributions are of
    sufficient quality, we may publish a journal special issue, edit a
    book, or do both.

    Paper submission

    We invite submission of long and short papers. Long papers should no
    longer than eight pages and short papers should be no longer than four
    pages, in the ICML-2002 format. Papers will be published
    electronically (and most probably also as ICML workshop notes). Please
    use the same instructions as for ICML-2002 papers, available at
    http://www.cse.unsw.edu.au/~icml2002/format.html.

    Electronic submissions should be either in PDF (preferred) or
    gzip-compressed PostScript. In addition, please send the title page in
    plain ASCII format.

    Submissions should be emailed both to Nada Lavrac (Nada.Lavrac@ijs.si)
    and to Tom Fawcett (tom_fawcett@hp.com)

    Important Dates

           22 April Paper submission deadline
           10 May Notification to participants
           31 May Camera ready copies
           5 June Working notes due
            9 July Workshop held

    Organizers

         Nada Lavrac
         J. Stefan Institute
         Jamova 39
         1000 Ljubljana
         Slovenia
         E-mail: nada.lavrac@ijs.si
         URL: http://www-ai.ijs.si/NadaLavrac/

         Hiroshi Motoda
         Division of Intelligent Systems Science,
         The Institute of Scientific and Industrial Research,
         Osaka University
         8-1 Mihogaoka, Ibaraki, Osaka 567-0047
         Japan
         E-mail: motoda@sanken.osaka-u.ac.jp
         URL: http://www.ar.sanken.osaka-u.ac.jp/motodaeg.html

         Tom Fawcett
         MS 1143
         Hewlett-Packard Laboratories
         1501 Page Mill Rd.
         Palo Alto, CA 94304
         USA
         E-mail: tom_fawcett@hp.com
         URL: http://www.hpl.hp.com/personal/Tom_Fawcett/

    Program committee

    Marko Bohanec marko.bohanec@ijs.si
    Andrea Danyluk andrea@cs.williams.edu
    Charles Elkan elkan@cs.ucsd.edu
    Dragan Gamberger dragan.gamberger@irb.hr
    Christophe Giraud-Carrier cgc@elca.ch
    Ross King rdk@aber.ac.uk
    Huan Liu hliu@asu.edu
    Dunja Mladenic dunja.mladenic@ijs.si
    Foster Provost fprovost@stern.nyu.edu
    Patricia Jean Riddle pat@cs.auckland.ac.nz
    Maarten van Someren maarten@swi.psy.uva.nl
    Ashwin Srinivasan Ashwin.Srinivasan@comlab.ox.ac.uk
    Einoshin Suzuki suzuki@dnj.ynu.ac.jp
    Shusaku Tsumoto tsumoto@computer.org



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