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Knowledge Discovery Nuggets(tm) 98:27, e-mailed 98-12-15


News:
  • (text) Gregory Piatetsky, Tools for Sequence Data Analysis ?
  • (text) Russ Greiner, Distribution of queries posed to belief net systems?

    Publications:
  • (text) Se June Hong, Special Issue on Data Mining: IEEE Intelligence
  • (text) Jiawei Han, SFU Graduate Student Research Theses on Data Mining
  • (text) Dunja Mladenic, PhD thesis on machine learning from large text data
  • (text) Maria Zemankova, NSF KDI CfP -- updated, IMPORTANT CHANGES
  • (text) Russ Greiner, SIGART/AAAI Doctoral Consortium, sub. deadline 5 Feb 99

    Positions:
  • (text) Pereric Lindquist, Market Analyst at MT&T, Halifax, Nova Scotia, Canada
  • (text) K. Burn-Thornton, Research Studentships at University of Plymouth, UK

    Courses:
  • (text) Rob Tibshirani, Modern Regression and Classification:
    Menlo Park, CA, Mar 1-2, 1999

    Meetings:
  • (text) David Heckerman, Workshop on AI and Statistics: Hotel deadline Dec 21, 1998,
    workshop: January 3-6, 1999, Ft. Lauderdale, Florida.
    http://uncertainty99.microsoft.com/
  • (text) Geraint Wiggins, 2nd CFP: Symposium on AI and Scientific Creativity,
    AISB'99 Convention, Edinburgh, Scotland, 6-9 April 1999,
    http://www.dai.ed.ac.uk/~simonco/conferences/AISB99
  • (text) Ronen Feldman, IJCAI-99 Workshop on Text Mining,
    Stockholm, Sweden, August 2, 1999
  • (text) Michael Berthold, IDA-99 Call for Papers,
    Amsterdam, The Netherlands, 9th-11th August 1999
    http://www.wi.leidenuniv.nl/~ida99/,
  • (text) RSFD, CFP: RSFDGrC'99: 7th Int. Workshop on Rough Sets, Fuzzy Sets,
    Data Mining, and Granular-Soft Computing,
    Yamaguchi, Japan, November 9-11, 1999
    http://ain2.ai.csse.yamaguchi-u.ac.jp/rsfdgrc99
  • (text) Matthias Klusch, CFP: Meeting of AgentLink SIG on Intelligent
    Information Agents, April 21 & 22, 1999 London (UK)
    http://www.informatik.tu-chemnitz.de/~klusch/SIGM2.html
  • (text) Miguel Feldens, CFP - WebVis'99, Web-Based Information Visualization,
    Florence, Italy, August 30 - September 3, 1999,
    http://www.informatik.uni-konstanz.de/swe/WebVis99.html
    --
    Knowledge Discovery Nuggets (TM) or KDNuggets for short, is an
    electronic newsletter focusing on the latest news, publications, tools,
    meetings, and other relevant items in the Data Mining and Knowledge Discovery
    field. KDNuggets is currently reaching over 6000 readers in 75+ countries
    2-3 times a month.

    Items relevant to data mining and knowledge discovery are welcome
    and should be emailed to gps in ASCII text or HTML format.
    An item should have a subject line which clearly describes
    what is it about to KDNuggets readers.
    Please keep calls for papers and meeting announcements
    short (50 lines or less of up to 80-characters), and provide a web site for
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    -- Gregory Piatetsky-Shapiro (editor)
    gps

    ********************* Official disclaimer ***************************
    All opinions expressed herein are those of the contributors and not
    necessarily of their respective employers (or of KD Nuggets)
    *********************************************************************

    ~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    That thought got ran over as it was crossing my mind.
    (thanks to http://www.geocities.com/SoHo/2439/quote.htm

    Previous  1 Next   Top
    Date: Tue, 8 Dec 1998 11:29:08 -0500
    From: Gregory Piatetsky-Shapiro gps
    Subject: Tools for Sequence Data Analysis

    Ismail Parsa has asked me about tools for finding Sequential Patterns, e.g.
    If after A there is B and C, then D is likely

    I am aware of research done in Helsinki PM/DM group
    http://www.cs.Helsinki.FI/research/pmdm/datamining/,
    and of several papers by Rakesh Agrawal in recent KDD proceedings,
    but there are relatively few generally available tools.

    Ismail has found the following tools:

    IBM Intelligent Miner
    SAS Enterprise Miner
    SRA KDD Explorer (they have more 'detection' than 'discovery')
    HyperParallel //Sequence
    NeoVista DecisionAR (I am not 100% sure of this one)

    which I have added to a new section in
    http://www.kdnuggets.com/siftware.html#SeqAssoc

    If you have additional information on tools, please reply to gps
    and I will summarize to the list.

    Previous  2 Next   Top
    Date: Fri, 4 Dec 1998 13:55:22 -0700
    From: Russ Greiner greiner@cs.ualberta.ca
    Subject: Query Distribution

    Dear Colleagues,

    There are now a number of deployed systems that use belief nets (aka bayesian
    nets, probability nets, ...) to answer queries -- ie, to compute the posterior
    probability of some variable(s), based on some specified set of evidence. It
    would be very useful to know the actual distribution of queries posed to such
    real-world systems; eg, how often the user asks
    'What is the probability of cancer, given Fever=T and Age>42 ?',
    vs
    'What is the probability of cancer, given Fever=F, lump=F and Gender=M ?'
    vs
    'What is the prior probability of hepatitis ?'
    etc etc etc.
    We could then use this 'query distribution' to evaluate our learning
    algorithms, by computing (perhaps) the

    *average (sum-squared) accuracy*

    of the belief net it returns, where the 'average' is wrt this
    real-world distribution (cf, [Greiner/Grove/Schuurmans, 'Learning
    Bayesian Nets that Perform Well', UAI-97]).

    We are therefore looking for some real-world *query distributions*.

    Please let me know if you can provide this information -- perhaps in the
    form of the set of queries actually posed to a real system, or a set of
    session transcripts or log files, of a system's interations with its users,
    or ...

    To avoid confusion, note that this QUERY DISTRIBUTION cannot necessarily be
    inferred from the given belief net B, as the query distribution might be
    completely unrelated to the 'NATURAL DISTRIBUTION' of events (encoded by B).
    Eg, we may ask many queries about low probability events --- the probability
    of the QUERY
    'What is the probability of cancer?'
    may be very high, even though the actual probability of
    Cancer
    is very low.

    Thank you.

    | Russell Greiner Phone: (403) 492-5461 |
    | Dep't of Computing Science FAX: (403) 492-1071 |
    | University of Alberta Email: greiner@cs.ualberta.ca |
    | Edmonton, AB T6G 2H1 Canada http://www.cs.ualberta.ca/~greiner/ |

    Previous  3 Next   Top
    Date: Tue, 8 Dec 1998 09:53:25 -0500
    From: Se June Hong, sjhong@us.ibm.com
    Subject: Special Issue on Data Mining: IEEE Intelligence
    Web: http://computer.org/Intelligent/

    IEEE Intelligent Systems magazine is planning to have a special
    issue on data mining in late 1999, in conjunction with a special
    track on data mining of IEEE Concurrency magazine to appear
    concurrently.

    Extracting and abstracting useful information from massive data
    is becoming increasingly important in many commercial and
    scientific domains. The process of data mining includes
    generating predictive models, clustering or segmenting database
    events into coherent groups, finding patterns, anomalities and
    trends, and other abstractions. The special issue will feature
    papers on data mining techniques with emphasis on practical
    usefulness, scalability, and capability to handle noisy data.

    Intelligent Systems will focus on machine learning applications
    while IEEE Concurrency will focus on systems issues. Intelligent
    Systems solicits papers on real applications based on data
    mining techniques: Bayesian Nets, Neural Nets, trees/rules,
    probablistic modelling, text mining, association rules, ILP,
    clustering and others. The domain of application can be scientific,
    business, or industry. Submitted papers will be coordinated
    with IEEE Concurrency and may be referred to each other as
    appropriate.

    Dates to remember
    08/23/99 Final copies due to the Publication office

    Access http://computer.org/Intelligent/ for more details about the
    submission process and complete author guidelines. We encourage
    electronic submissions. Send submissions to our Magazine Assistant,

    Molly Davis
    IEEE Intelligent Systems
    Computer Society
    10662 Los Vaqueros Circle
    Los Alamitos, Calif 90720
    mdavis@computer.org

    Please indicate clearly that the submission is for IEEE Intelligent
    Systems' Special Issue on Data Mining.

    Guest Editors:
    David Waltz, NEC Research, waltz@research.nj.nec.com
    Se June Hong, IBM Research, sjhong@us.ibm.com

    Previous  4 Next   Top
    Date: Wed, 9 Dec 1998 11:43:37 -0800 (PST)
    From: Jiawei Han han@cs.sfu.ca
    Subject: SFU Graduate Student Research Theses on Data Mining (1997-1998)
    Web: http://db.cs.sfu.ca

    All the theses are in postscript form. To fetch them, please go to
    http://db.cs.sfu.ca, click and then .

    -----------------------------
    Hua Zhu, `` On-Line Analytical Mining of Association Rules '', M.Sc. thesis,
    Computing Science, Simon Fraser University, December 1998.

    Yin Jenny (Chiang) Tam, `` Datacube: Its Implementation and Application in
    OLAP Mining '', M.Sc. thesis, Computing Science, Simon Fraser University,
    September 1998.

    Gabor Melli, `` A Lazy Model-Based Approach to On-Line Classification '',
    M.Sc. thesis, Computing Science, Simon Fraser University, April 1998.

    Shan Cheng, `` Statistical Approaches to Predictive Modeling in Large
    Databases '', M.Sc. thesis, Computing Science, Simon Fraser University,
    March 1998.

    Yijun Lu, `` Concept Hierarchies in Data Mining: Specification, Generation
    and Application'', M.Sc. thesis, Computing Science, Simon Fraser University,
    January 1998.

    Wan Gong, `` Periodic Pattern Search in Time-Related Data Sets'',
    M.Sc. thesis, Computing Science, Simon Fraser University, December 1997.

    Betty Bin Xia, `` Similarity Search in Time Series Data Sets'', M.Sc. thesis,
    Computing Science, Simon Fraser University, December 1997.

    Nebojsa Stefanovic, `` Design and Implementation of On-Line Analytical
    Processing (OLAP) of Spatial Data'', M.Sc. thesis, Computing Science,
    Simon Fraser University, September 1997.

    Previous  5 Next   Top
    Date: Fri, 11 Dec 1998 13:27:22 +0100
    From: Dunja Mladenic Dunja.Mladenic@ijs.si
    Subject: PhD thesis on machine learning from large text data
    Web: http://www.cs.cmu.edu/~TextLearning/pww/PhD.html
    or http://www-ai.ijs.si/DunjaMladenic/PhD.html

    I'm glad to announce that a PhD thesis
    on machine learning from large text data is available at
    http://www.cs.cmu.edu/~TextLearning/pww/PhD.html
    (or at http://www-ai.ijs.si/DunjaMladenic/PhD.html

    This dissertation proposes new elements of machine learning
    methods where the corresponding learning problem is characterized
    by a high number of features (several tens of thousands),
    unbalanced class distribution (less than 1%-10% of examples belong
    to the target class value) and asymmetric misclassification costs.
    Automatic document categorization using the proposed methods was
    performed on real-world data obtained from the Yahoo hierarchy of Web
    documents (see demo at http://www-ai.ijs.si/DunjaMladenic/yplanet.html.

    Best regards,
    Dunja Mladenic

    Previous  6 Next   Top
    Date: Sun, 6 Dec 1998 15:33:22 -0500
    From: Maria Zemankova mzemanko@nsf.gov
    Subject: NSF KDI CfP -- updated, IMPORTANT CHANGES
    Web: http://www.nsf.gov/cgi-bin/getpub?nsf9929

    Updated information is now available on the NSF Online
    Document System for the following document (nsf9929):

    Title: Knowledge and Distributed Intelligence (KDI) Proposal
    Solicitation
    Type: Program Announcements & Information
    Subtype: Crosscutting Programs, NSF-wide

    FASTLANE is now required for the submission of both preproposals and full
    proposals. Please note the changes in the section on proposal submission.

    It may be found at:

    http://www.nsf.gov/cgi-bin/getpub?nsf9929

    --
    NSF Custom News Service
    http://www.nsf.gov/home/cns/start.htm
    Please send questions and comments to webmaster@nsf.gov

    Previous  7 Next   Top
    Date: Fri, 4 Dec 1998 13:44:31 -0700
    From: Russ Greiner greiner@cs.ualberta.ca
    Subject: SIGART/AAAI Doctoral Consortium
    Web: http://www.aaai.org/Conferences/National/1999/aaai99-dccall.html

    The SIGART/AAAI Doctoral Consortium is a great opportunity for PhD
    students to receive feedback on their research and network with
    people in the field. Accepted participants will receive travel
    scholarships and free registration to AAAI-99. The call for
    participation is at:

    http://www.aaai.org/Conferences/National/1999/aaai99-dccall.html

    Note that submissions are due 5 February 1999.

    Previous  8 Next   Top
    Date: Mon, 07 Dec 1998 09:06:11 -0800
    From: Pereric Lindquist plindquist@mtt.ca
    Subject: Market Analyst at MT&T, Halifax, Nova Scotia, Canada

    MARKET ANALYST (REF.# 98-118)

    You have strong marketing and analytical skills with a background
    in statistical analysis and statistical techniques such as
    regression analysis,clustering,and neural networks. You are self
    motivated and thrive working in a team environment. You have
    excellent relationship building skills and collaboration skills
    which allow you to manage vendor and supplier relationships, as
    well as work with the marketing team to create a better
    understanding of customer needs.

    Day-to-Day Role: As a member of the Consumer Knowledge Creation team
    you will:

    - Facilitate the use of customer understanding
    (segmentation, models and queries) on project teams related to the
    development and marketing of new products and services including
    determination of target markets, market research and generating
    customer lists for campaign purposes.

    - Develop and refine segmentation models to assist business and marketing strategies
    - Develop predictive and propensity models to assist in the targeting of new and existing products and services
    - Support the marketing team and other users of the marketing database in queries and reporting
    - Work with the Marketing Database Administrator to ensure the database continues to evolve to meet the needs of the SMA
    - Support other Analysts in the area of understanding market trends, competitors and business unit performance

    'Must Have' Skills:
    -Analytical skills with background in statistical analysis and statistical techniques
    -Database querying capability, e.g. experience writing queries
    -Marketing experience and/or knowledge
    -Strong written and communications skills
    -Relationship building and collaboration skills
    -Excellent organizational and coordination skills

    'Nice to Have' Skills:
    -MBA specializing in Econometrics/Statistics or Masters of Applied Science
    -Data base marketing application experience
    -Modeling and analysis experience with SAS and SPSS
    -Project Management skills

    You can submit your resume, complete with cover letter,
    indicating the position you are interested in to MT&T via any one of
    the following methods:

    Email: jobs@mtt.ca (Microsoft Word or text documents only please)
    Fax: 1-888-317-1101 (Canada-wide)
    Mail: MT&T Human Resource Centre
    P.O. Box 880, Station Central RPO
    Halifax, Nova Scotia
    B3J 2W3
    �
    Previous  9 Next   Top
    Date: Fri, 11 Dec 1998 09:57:05 GMT
    From: K. Burn-Thornton kburnt@soc.plym.ac.uk
    Subject: Research Studentships at University of Plymouth, UK

    University of Plymouth, School of Computing

    Applications are invited for the following two Research Studentships
    within the Data Mining Group, from 01/02/99, for a period of three
    years, subject to satisfactory progress.


    1) The use of Data Mining to Analyse Faults in Assembled Products from X-Ray
    Images

    An Enhanced EPSRC/FARADAY Research Studentship is available with a
    current bursary of #5,805 per annum and with an additional �2,500 per
    annum contributed by the industrial partner, Image Scan Holdings. A
    further �5,000 is available each year from the DTI for training.

    Candidates should possess a good first-degree (2.1) or a relevant MSc,
    in Engineering, Computer Science or Mathematics. A knowledge of Data
    Mining techniques or Functional Programming would be an advantage.

    2) The use of Data Mining for Pro-active Network Management

    The current bursary for this Wandel & Golterman funded studentship is
    �6,455 per annum.

    Candidates should possess a good first degree (2.1) or a relevant MSc,
    in Engineering, Computer Science or Mathematics. A knowledge of
    Machine Learning or Data Mining techniques would be an advantage.

    Informal enquiries may be made to Dr K Burn-Thornton, 01752 232621,
    email: Kburn-Thornton@plym.ac.uk. Applications forms available from
    Mrs C Watson, 01752 232541, email Carole@soc.plym.ac.uk

    CLOSING DATE: 11 January 1999

    Previous  10 Next   Top
    Date: Wed, 9 Dec 1998 11:23:47 -0800 (PST)
    From: Rob Tibshirani tibs@stat.Stanford.EDU
    Subject: Modern Regression and Classification: Menlo Park, CA, Mar 1-2, 1999
    Web: http://www-stat.stanford.edu/~trevor/mrc.html
    ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    +++ Modern Regression and Classification: +++
    +++ +++
    +++ Widely applicable statistical methods +++
    +++ for modeling and prediction +++
    +++ +++
    +++ Stanford Park Hotel, Menlo Park, CA Mar 1-2, 1999 +++
    +++ +++
    +++ Trevor Hastie & Rob Tibshirani, Stanford University +++
    ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    This two-day course will give a detailed overview of statistical models
    for regression and classification. Known as machine-learning in computer
    science and artificial intelligence, and pattern recognition in engineering,
    this is a hot field with powerful applications in finance, science and industry.
    This is a very popular course, normally offered only twice a year in the U.S.

    At our the recent course in Chicago, participants were asked
    to rate the course on a scale from 1 (poor) to 5 (outstanding).
    The median score was 5!

    Info and registration forms: http://www-stat.stanford.edu/~trevor/mrc.html
    or email to trevor@stat.stanford.edu, tibs@stat.stanford.edu
    These courses fill up quickly, so sign up early to ensure a spot.

    Previous  11 Next   Top
    Date: Thu, 10 Dec 1998 13:00:03 -0800
    From: David Heckerman heckerma@MICROSOFT.com
    Subject: Workshop on AI and Statistics: Hotel deadline Dec 21, 1998
    Web: http://uncertainty99.microsoft.com/

    This is a reminder that Uncertainty 99: The Seventh International Workshop
    on Artificial Intelligence and Statistics will be held January 3-6, 1999
    in Ft. Lauderdale, Florida.

    After December 21 1998, the hotel (The Radisson Bahia Mar Beach Resort) will
    no longer withhold a block of rooms for this conference, so make your
    reservations soon!

    Registration forms, the conference program, an online proceedings, and
    other details about the conference and the Society for Artificial
    Intelligence and Statistics can be found at
    http://uncertainty99.microsoft.com/.

    David Heckerman and Joe Whittaker,
    Conference Chairs


    Previous  12 Next   Top
    Date: Thu, 03 Dec 1998 19:10:55 +0000
    From: Geraint Wiggins geraint@dai.ed.ac.uk
    Subject: 2nd Call for papers: Symposium on AI and Scientific Creativity
    Web: http://www.dai.ed.ac.uk/~simonco/conferences/AISB99

    SYMPOSIUM ON AI AND SCIENTIFIC CREATIVITY
    at the AISB'99 Convention, 6th-9th April 1999
    Edinburgh College of Art &
    Division of Informatics, University of Edinburgh

    The AISB'99 Convention will be held in Edinburgh in April 1999. It will
    consist of 13 workshops and symposia on a wide range of themes in
    Artificial Intelligence and Cognitive Science. An underlying theme of
    the Convention this year is the study of creativity, though not all of
    the events include a creative element. Further details of AISB'99 will
    be found at the conference web site, listed below.

    Paper submissions are invited for the Symposium on AI and Scientific
    Creativity.

    Programs using AI techniques are now successful in many scientific
    domains, including astronomy, biology, chemistry, mathematics, medicine
    and physics. This success has led to a strong interest in automating
    aspects of scientific creativity, including (i) making new definitions
    and categorisations, (ii) spotting empirical facts and making
    hypotheses, (iii) designing experiments, (iv) finding examples of a
    phenomenon and (v) making explicit assumed facts.

    This symposium aims to identify some core notions of machine discovery
    in science, as addressed by the 1995 AAAI spring symposium on
    scientific discovery and the 1998 ECAI machine discovery workshop,
    amongst others. We need to understand the computational frameworks,
    psychological and philosophical models available for machine creativity
    in science, and the programs designed by AI researchers and domain
    scientists for scientific discovery. The areas of interest of the {f
    ame} will include, but are not limited to:

    * Scientific discovery programs and results from particular domains;
    * philosophical discussions and case studies of scientific creativity;
    * machine learning techniques, such as ILP, and computational
    approaches to scientific creativity;
    * data mining approaches to knowledge extraction from scientific data.

    We hope to promote an exchange of ideas between people proposing models
    and frameworks for automated scientific creativity and those who are
    implementing and testing creative programs in scientific domains.

    Submission of Extended Abstracts 21 December '98
    Please see the symposium web page at
    http://www.dai.ed.ac.uk/~simonco/conferences/AISB99
    for further details.

    Previous  13 Next   Top
    Date: Mon, 7 Dec 1998 00:30:28 +0200
    From: Ronen Feldman ronen@instinct-soft.com
    Subject: IJCAI-99 Workshop on Text Mining

    IJCAI-99 Workshop on Text Mining
    TEXT MINING: FOUNDATIONS, TECHNIQUES AND APPLICATIONS
    Stockholm, Sweden
    August 2, 1999

    The information age has made it easy to store large amounts of data.
    The proliferation of documents available on the Web, on corporate
    intranets, on news wires, and elsewhere is overwhelming. However,
    while the amount of data available to us is constantly increasing,
    our ability to absorb and process this information remains constant.
    Search engines only exacerbate the problem by making more and more
    documents available in a matter of a few key strokes; so-called 'push'
    technology makes the problem even worse by constantly reminding us
    that we are failing to follow critical news, events, and trends. We
    experience information overload, missing important patterns even as
    they unfold before us.

    Text Mining is a new and exciting research area that tries to solve
    the information overload problem by using techniques from data mining,
    machine learning, information retrieval, natural-language
    understanding, case-based reasoning, statistics, and knowledge
    management to help people gain insight into large quantities of
    semi-structured or unstructured text. Text Mining typically involves
    preprocessing of a document collection (such as through text
    categorization or term extraction), storage and indexing of the
    intermediate representations, analysis of the intermediate
    representations (such as via distribution analysis, document
    clustering, trend analysis, and association rule discovery), and
    visualization of the results. Sample topics appropriate for this
    workshop include the development of efficient algorithms for very
    large document collections, tools for visualizing such document
    collections, the use of intelligent agents to perform text mining on
    the internet, and the use information extraction to better capture the
    major themes of the documents. More generally, we solicit papers in
    all areas relevant to the problem of gaining insight into large
    collections of text, including, but not limited to, the following
    areas:

    * Association Rule Discovery from Document Collections
    * Document Representations
    * Information Extraction for Text Mining
    * Multi-lingual Text Mining
    * Storage Issues
    * Taxonomy Generation for Text Mining
    * Term Extraction
    * Text Categorization
    * Text Mining Applications
    * Text Mining on the Internet
    * Trend Analysis
    * Visualization Techniques

    [edited. GPS]
    * Submission deadline: 15 April 1999

    Send submissions and request for more information to

    Ronen Feldman
    Director, Data Mining Laboratory
    Department of Mathematics and Computer Science
    Bar-Ilan University
    Ramat-Gan, ISRAEL, 52900
    (972) 3-5318629 (tel)
    (972) 3-5353325 (fax)
    Email: feldman@cs.biu.ac.il


    Previous  14 Next   Top
    Date: Tue, 1 Dec 1998 11:47:06 -0800 (PST)
    From: Michael Berthold berthold@ICSI.Berkeley.EDU
    Subject: IDA-99 Call for Papers
    Web: http://www.wi.leidenuniv.nl/~ida99/

    IDA-99
    The Third International Symposium on Intelligent Data Analysis
    Center for Mathematics and Computer Science, Amsterdam, The Netherlands
    9th-11th August 1999

    Call for papers
    ===============
    IDA-99 will take place in Amsterdam from 9th to 11th August 1999, and is
    organised by Leiden University in cooperation with AAAI, CEPIS, and NVKI.
    It will consist of stimulating invited talks by Jacqueline Meulman (Optimal
    Scaling), Zdzislaw Pawlak (Rough Sets), and Paul Cohen (Data Analysis and
    the Development of Robot Minds). The international Program Committee will
    carefully review submitted papers, combining the selected ones into a
    single-track program, consisting of oral presentations and poster sessions.
    The aim is for IDA-99 to bring together a wide variety of researchers
    concerned with extracting knowledge from data, including people from
    statistics, machine learning, neural networks, computer science, pattern
    recognition, database management, and other areas. The strategies adopted by
    people from these areas are often different, and a synergy results if this
    is recognised. IDA-99 is intended to stimulate interaction between these
    different areas, so that more powerful tools emerge for extracting knowledge
    from data and a better understanding is developed of the process of
    intelligent data analysis.

    It is the third symposium on Intelligent Data Analysis after the successful
    symposia Intelligent Data Analysis 97 http://www.dcs.bbk.ac.uk/ida97.html/
    and Intelligent Data Analysis 95.

    IDA-99 Organisation
    ===================
    General Chair: David Hand, Open University, UK
    Program Chair: Joost Kok, Leiden University, The Netherlands
    Program Co-Chairs: Michael Berthold, University of California, Berkeley, USA
    Doug Fisher, Vanderbilt University

    Important Dates
    ===============
    February 1st, 1999 Deadline for submitting papers
    April 15th, 1999 Notification of acceptance
    May 15th, 1999 Deadline for submission of final papers

    Publications
    ============
    The proceedings will be published in the Lecture Notes in Computer Science
    series of Springer. The proceedings of IDA-97 appeared as LNCS 1280.
    http://www.springer.de/comp/lncs/volumes/1280.htm

    Additional Information
    ======================
    A list of topics of interest, guidelines for submissions, and information
    about the conference-site can be found on the World Wide Web Server of the
    Leiden Institute for Advanced Computer Science:
    http://www.wi.leidenuniv.nl/~ida99/



    Previous  15 Next   Top
    Date: Thu, 3 Dec 98 18:43:52 JST
    From: RSFD, rsfdgrc99@ai.csse.yamaguchi-u.ac.jp
    Subject: RSFDGrC'99: CALL FOR PAPERS
    Web: http://ain2.ai.csse.yamaguchi-u.ac.jp/rsfdgrc99

    The Seventh International Workshop on
    Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing
    ----------------------------------------------------------------
    Yamaguchi Resort Center, Ube, Yamaguchi, Japan
    ==============================================
    November 9-11, 1999

    Home Page: http://ain2.ai.csse.yamaguchi-u.ac.jp/rsfdgrc99
    Papers Due: May 20, 1999

    Organized by
    International Rough Set Society
    BISC Special Interest Group on Granular Computing (GrC)
    Polish-Japanese Institute of Computer Techniques
    Yamaguchi University
    ACM SIGKDD

    The Seventh International Workshop on Rough Sets, Fuzzy Sets, Data
    Mining, and Granular-Soft Computing (RSFDGrC'99) will provide an
    international forum for the sharing of original research results and
    practical development experiences among experts in these emerging
    fields. An important feature of the workshop is to stress the
    integration of intelligent information techniques. That is, promoting
    deep fusion of these emerging techniques in AI, Soft Computing, and
    Database communities for solving real world, large, complex problems
    with uncertainty and fuzziness. In particular, fuzzy and rough set
    methods in data mining and granular computing. We also look for
    contributions in related fields that include, but are not limited to,
    the following areas:

    - Rough Set Theory and Its Applications
    - Fuzzy Set Theory and Its Applications
    - Data Mining and Data Warehousing
    - Knowledge Creation and Discovery
    - Information Granulation and Granular Computing
    - Computing with Words
    - Machine Learning
    - Neural Networks
    - Evolutionary Computing
    - Probabilistic and Statistical Reasoning
    - Approximate Reasoning
    - Uncertainty Management
    - Non-Classical Logic and Set Theories
    - Database Reverse Engineering
    - Data and Dimensionality Reduction
    - Deep Fusion of Computational and Symbolic Processing
    - Intelligent Information Retrieval
    - Information Discovery on the Internet
    - Decision Support Systems
    - Hybrid and Integrated Intelligent Systems
    - Intelligent Agent and Multi-Agent Systems
    - Soft Computing and Its Applications

    For more information, contact

    Prof. Ning Zhong (RSFDGrC'99)
    Department of Computer Science and Systems Engineering
    Faculty of Engineering, Yamaguchi University
    Tokiwa-Dai, 2557, Ube 755, Japan
    Telephone & Fax: +81-836-35-9949
    Email: zhong@ai.csse.yamaguchi-u.ac.jp

    or see http://ain2.ai.csse.yamaguchi-u.ac.jp/rsfdgrc99
    [edited GPS]

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    Date: Fri, 04 Dec 1998 12:56:20 -0500
    From: Matthias Klusch klusch@cs.cmu.edu
    Subject: CFP: Meeting of AgentLink SIG on Intelligent Information Agents
    Web: http://www.informatik.tu-chemnitz.de/~klusch/SIGM2.html

    CALL FOR PARTICIPATION
    Second Meeting of the AgentLink Special Interest Group on
    INTELLIGENT INFORMATION AGENTS
    April 21 & 22, 1999 London (UK)

    The main aim of this special interest group (SIG) is to promote
    collaborative projects and cross fertilisation of ideas between
    academic nodes with similar interests in the research area of
    INTELLIGENT INFORMATION AGENTS (I2A).
    This shall be done, e.g., by putting groups with related interests in
    touch with one-another, providing and disseminating information
    about work of national and international groups and projects in the
    I2A area, supporting workshops and conferences of interest.

    For more details about the I2A-SIG, please, see the SIG's home page in
    the Web, bookmark it and check back often for up-to-date informations:

    http://www.informatik.tu-chemnitz.de/~klusch/i2a-SIG.html

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    Date: Fri, 11 Dec 1998 13:53:39 -0300
    From: Miguel Feldens MFeldens@ucs.tche.br
    Subject: CFP - WebVis'99
    Web: http://www.informatik.uni-konstanz.de/swe/WebVis99.html
    CALL FOR PAPERS: WebVis '99
    International Workshop on
    Web-Based Information Visualization

    in conjunction with the
    10th International Conference on
    Database and Expert Systems Applications (DEXA'99)
    Florence, Italy, August 30 - September 3, 1999

    Workshop proceedings to be published by IEEE Computer Society Press


    Information visualization combines aspects of scientific visualization,
    human-computer interaction, data mining, imaging and graphics.
    It focuses on information which is often abstract. This means that many
    interesting classes of information have no natural and obvious physical
    metaphors for representing information and to understand which
    analytical tasks they support. The largest information space is perhaps the
    World Wide Web, which
    contains millions of pages. Information visualization in this domain
    enables users to get information quickly, put it in a meaningful shape,
    and to make decisions in a short time. Web-based information
    visualization describes visualization applications that use the Web as
    an information source, a delivery mechanism for visualization, or both.

    The aim of this workshop is to bring together researchers and
    practitioners who are working in key technology areas of Information
    Visualization in order to discuss recent research findings and address
    complementary research and development issues. Of particular interest
    are papers describing different visualization techniques to make use of
    the information available in the net or how Web-techniques can be used
    to visualize information.

    IMPORTANT DATES
    Submission deadline: .................. March 30, 1999

    Submission and other details -- please see the website
    http://www.informatik.uni-konstanz.de/swe/WebVis99.html
    [edited GPS]

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