Knowledge Discovery Nuggets 97:12, e-mailed 97-04-10

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Knowledge Discovery Nuggets 97:12, e-mailed 97-04-10

News:
* E. Colet, Advanced Scout News --
  • http://www.nextstep.com/new_this_week/120/advancedscout.html

    • * A. Andrusiewicz, Query -- Mining Association Rules
    Publications:
    * H. Motoda, Final CFP: IEEE Expert Special Issue on
    Feature Transformation and Subset Selection
    Siftware:
    * O. Leng, WinViz for Excel,
  • http://jsaic.iti.gov.sg/projects/vizMain.html

    • Positions:
    * W. Jones, Knowledge Discovery Research at U. of Alabama at Birmingham
    (UAB),
  • http://www.cis.uab.edu/info/kdrg/kdrg.html

    • * R. Straughan, Senior Consultant in Data Mining at NSRC in Singapore
  • http://www.nsrc.nus.sg

    • Meetings:
    * R. Tibshirani, Modern Regression and Classification course,
    New York , June 23-24, 1997
  • http://stat.stanford.edu/~trevor/mrc.finance.html

    • * PADD97, Practical Application of Knowledge Discovery and Data Mining
    Conference Program, London, 23-25 April 1997,
  • http://www.demon.co.uk/ar/PADD97/

    • * M. Conkling, Data Warehousing Best Practices & Implementation Conference
    Chicago May 27-June 1, 1997,
  • http://www.dw-institute.com/

    • * GPS, Data Mining'97 : Increasing Corporate Performance,
    Paris, June 2-4, 1997, cancelled
    --
    Knowledge Discovery Nuggets is a free electronic newsletter for the
    Data Mining and Knowledge Discovery community, focusing on the
    latest research and applications.

    Submissions are most welcome and should be emailed, with a
    DESCRIPTIVE subject line (and a URL) to gps.
    To subscribe, see
  • http://www.kdnuggets.com/subscribe.html


    • KD Nuggets frequency is 3-4 times a month.
    Back issues of KD Nuggets, a catalog of data mining tools
    ('Siftware'), pointers to Data Mining Companies, Relevant Websites,
    Meetings, and more is available at Knowledge Discovery Mine site
    at
  • http://www.kdnuggets.com/


    • -- Gregory Piatetsky-Shapiro (editor)

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

    ~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    No matter how neutral the topic, your message will offend SOMEONE.
    Murphy's laws of BBS, thanks to
  • http://www.calweb.com/~logon/murphy.html

    • Previous  1 Next   Top
    From: 'Edward Colet'(ecolet@watson.ibm.com)
    Date: Wed, 26 Mar 1997 16:30:56 -0400
    Subject: Advanced Scout

    Readers may be interested in some recent updates on the data mining/KDD
    work of IBM Research's Advanced Scout Project (the data mining application
    used in the National Basketball Association). These can be found in
    newspapers, TV, the web and the SIGMOD/PODS schedule. Specifically, the
    press coverage of Advanced Scout appeared in the Los Angeles Times,
    2/17/97, page C4. Also, the TV show, 'NextStep' showed a feature on
    Advanced Scout that aired in the San Francisco area on 3/8/97. A broadcast
    of this feature will air nationwide on the Discovery channel at a later
    date. The URL for the NextStep feature called 'Hard-wired Hoops' can be
    found at :
  • http://www.nextstep.com/new_this_week/120/advancedscout.html


    • Also available on the Web is an online posting containing the abstract and
    bio for the keynote address on data mining at SIGMOD/PODS, 1997 to be given
    by Inderpal. The URL is:
  • http://mundos.ifsm.umbc.edu/~ramesh/sigmod97/advprog.html.
    • It's
    accessible from within both the SIGMOD or the PODS schedules.

    Thanks,
    Ed Colet.

    *********************************************
    IBM T.J. Watson Research Center
    30 Saw Mill River Road
    Hawthorne NY 10532
    phone: 914-784-6621; tie-line 863
    fax: 914-784-7455
    email: ecolet@watson.ibm.com
    *********************************************
    Previous  2 Next   Top
    Date: Thu, 27 Mar 1997 12:04:21 +1000 (EST)
    From: Anna Andrusiewicz (annaa@it.uq.edu.au)

    Hi,

    I am working on a problem that may be related to mining generalized
    association rules. The basic problem involves mining student enrolment
    histories in order to figure out what subjects are being taken by what
    kinds of students.

    I would like to conduct a case study on the enrolments data I have, and
    was wondering if anyone knows of a public domain system for mining
    association, or multi-level association rules.

    Any help offered will be much appreciated - thank you,

    Anna Andrusiewicz
    School of Information Technology
    The University of Queensland, Australia

    Previous  3 Next   Top
    From: motoda@sanken.osaka-u.ac.jp
    Subject: Final Call for Papers: IEEE Special Issue
    Date: Sat, 29 Mar 97 17:13:06 +0900

    Final Call For Papers

    IEEE Expert Special Issue on

    Feature Transformation and Subset Selection

    Guest Editors: Huan Liu and Hiroshi Motoda

    (edited for space ... see Nuggets 96:37 for full CFP
  • http://www.kdnuggets.com/news/96/n37.html#item4


    • III. SUBMISSION REQUIREMENTS and SCHEDULE

    High quality, original papers that deal with real-world problems
    are solicitated. All the submitted manuscripts will be subject
    to a rigorous review process. Manuscripts should be prepared in
    accordance with the IEEE Expert 'submission guidelines'.
    Manuscripts should be approximately 5,000 words long, preferably
    not exceeding 10 references. This special issue is scheduled to
    appear in late 1997.

    Important Dates:

    Submission April 30 (FIRM DEADLINE)

    Notification June 30

    Prospective authors should submit six copies of the completed
    manuscript to one of the guest editors:

    Huan Liu Hiroshi Motoda
    S16 #4-17 Institute of Scientific & Industrial
    Dept of Info Sys & Comp Sci Research
    National University of Singapore Osaka University
    Kent Ridge, Singapore, 119260 Ibaraki, Osaka 567, Japan
    liuh@iscs.nus.sg motoda@sanken.osaka-u.ac.jp

    Previous  4 Next   Top
    Date: Sat, 29 Mar 1997 12:08:21 +0800
    From: Ong Hwee Leng (hweeleng@iti.gov.sg)
    Subject: WinViz for Excel

    A version of WinViz which runs with Excel 7.0 on Win95 is available for
    sale. WinViz is a multi-dimensional visualisation tool developed at the
    Information Technology Institute. More info & self-running demos can be
    found at
  • http://jsaic.iti.gov.sg/projects/vizMain.html


    • -Hwee-Leng Ong

    Previous  5 Next   Top
    Date: Mon, 24 Mar 1997 09:39:26 +0600
    From: jones@cis.uab.edu (Warren Jones)

    Knowledge Discovery Research at University of Alabama at Birmingham (UAB)
  • http://www.cis.uab.edu/info/kdrg/kdrg.html


    • This multidisciplinary research group is concentrating on healthcare applications,
    specifically on surveillance problems. The group consists of representatives from
    Computer and Information Sciences, Pathology and Health Informatics. A tool called
    Hawkeye has been developed which searches temporally organized medical data,
    builds associations and applies interestingness heuristics for the identification
    of trends of interest to medical domain experts. Hawkeye is also an example of a
    large scalable KDD system which requires the utilization of all stages of the KDD
    process. One of the important surveillance problems being investigated is the
    spread of antibiotic resistance.

    This Group provides a very attractive opportunity for UAB computer science
    graduate students to become involved in KDD research with a medical emphasis.
    Four Ph.D. students are currently associated with the Group and its on-going
    research. Graduate Assistantships are available for prospective Ph.D.students who are interested in entering the program Fall 1997 with a research interest in
    the directions of the Knowledge Discovery Research Group.

    UAB is a comprehensive urban institution in Alabama's largest city of almost a
    million population. Student enrollment exceeds 16,400, including more than
    3,500 graduate students. The Academic Health Center is well-known for its
    interdisciplinary biomedical research. The computer science graduate program
    has an enrollment of 50, half of which are Ph.D. students. The campus encompasses
    a seventy-block area on Birmingham's Southside, offering all of the advantages of a university within a major city.

    Warren T. Jones, Ph.D. Chair
    Department of Computer and Information Sciences
    University of Alabama at Birmingham
    Birmingham, AL 35294-1170
    Ph: (205)934-8657
    Fax: (205)934-5473
    jones@cis.uab.edu

    Previous  6 Next   Top
    From: Robert Straughan (rob@nsrc.nus.sg)
    Subject: Senior Consultant in Data Mining at NSRC in Singapore
    Date: Sat, 5 Apr 1997 09:06:47 +0800 (SGT)

    Staff Title: Group Leader - Senior Consultant, Commercial Applications
    Date Required: 1 June 1997

    Job Description: National Supercomputing Research Centre (NSRC) is
    Singapore's national centre for High Performance Computing (HPC). NSRC
    currently facilitates services and solutions to the Singapore industry
    in the field of Computer Aided Engineering, Chemical Applications and
    Electronics. Commercial Applications has been identified as a new
    growth area, where HPC can make a significant impact on the commercial
    industries' competitiveness. NSRC has therefore decided to expand into
    this field and is currently looking for a person with extensive
    industrial experience in the field of Data Mining within finance,
    banking, insurance, or retail marketing. The Group Leader shall take
    overall responsibility in promoting NSRC's capabilities within the
    field of Data Mining to the commercial industry in Singapore and to
    solicit for business. The Group Leader shall work closely with NSRC's
    existing staff within this field to develop the best possible strategy
    to target potential commercial organisations.

    Skills Required: Minimum Masters Degree. Specialisation within the
    field of Computer Science and Business Administration. At least 5
    years experience from a financial institution or in retail marketing
    within the field of Data Mining / Data Analysis. Extensive managerial
    experience, in particular project management, business analysis and
    negotiation skills. Strong knowledge of statistical analysis and
    selection / building of appropriate modelling techniques to solve
    business problems. A good understanding of the algorithms used in Data
    Mining (neural networks, classifications etc.). Have previously used
    IBM SP2 and tools such as Intelligent Miner and Darwin as well as
    statistical packages such as SAS and SPSS.

    Relocation assistance, allowances for housing, children's education and
    transportation apply. Salary will be commensurate with qualifications
    and experience.

    You can obtain more details by contacting admin@nrsc.nus.sg or visit
    our web site at
  • http://www.nsrc.nus.sg.


    • Resumes can be sent to:

    Administration Manager
    NSRC
    89 Science Park Drive
    The Rutherford #01-05/08
    Singapore 118261

    Previous  7 Next   Top
    From: tibs@utstat.toronto.edu
    Date: Sun, 23 Mar 97 22:45 EST
    Subject: Modern Regression and Classification course - New York
    ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    +++ +++
    +++ Modern Regression and Classification: +++
    +++ +++
    +++ Statistical prediction methods for finance +++
    +++ and marketing +++
    +++ +++
    +++ +++
    +++ New York City: June 23-24, 1997 +++
    +++ +++
    +++ Trevor Hastie, Stanford University +++
    +++ Rob Tibshirani, University of Toronto +++
    +++ +++
    ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


    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 course covers a wide range of models from linear regression
    through various classes of more flexible models to fully nonparametric
    regression models, both for the regression problem and for
    classification.

    This special version of our popular MRC course is tailored to financial
    and marketing professionals.

    Although a firm theoretical motivation will be presented, the emphasis
    will be on practical applications and implementations, especially in
    the finance and marketing areas. The course will include many examples
    and case studies, and participants should leave the course well-armed
    to tackle real problems with realistic tools. The instructors are at
    the forefront in research in this area.

    After a brief overview of linear regression tools, methods for
    one-dimensional and multi-dimensional smoothing are presented, as well
    as techniques that assume a specific structure for the regression
    function. These include splines, wavelets, additive models, MARS
    (multivariate adaptive regression splines), projection pursuit
    regression, neural networks and regression trees. All of these can be
    adapted to the time-series framework for predicting future trends from
    the past.

    The same hierarchy of techniques is available for classification
    problems. Classical tools such as linear discriminant analysis and
    logistic regression can be enriched to account for nonlinearities and
    interactions. Generalized additive models and flexible discriminant
    analysis, neural networks and radial basis functions, classification
    trees and kernel estimates are all such generalizations. Other
    specialized techniques for classification including nearest- neighbor
    rules and learning vector quantization will also be covered.

    Apart from describing these techniques and their applications to a wide
    range of problems, the course will also cover model selection
    techniques, such as cross-validation and the bootstrap, and diagnostic
    techniques for model assessment.

    Software for these techniques will be illustrated, and a comprehensive
    set of course notes will be provided to each attendee.

    Additional information is available at the Website:

  • http://stat.stanford.edu/~trevor/mrc.finance.html


    • ************************************************************
    Some quotes from past attendees:

    '... the best presentation by professional statisticians I have
    ever had the pleasure of attending'
    'Superior to most courses in all aspects'
    'I really liked how you emphasized concepts rather than
    mathematical expressions'
    'Your 2-day course has saved me months of research'
    *************************************************************

    ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    Rob Tibshirani, Dept of Preventive Med & Biostats, and Dept of Statistics
    Univ of Toronto, Toronto, Canada M5S 1A8.
    Phone: 416-978-4642 (PMB), 416-978-0673 (stats). FAX: 416 978-8299
    computer fax 416-978-1525 (please call or email me to inform)
    tibs@utstat.toronto.edu.
  • http://www.utstat.toronto.edu/~tibs

    • +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    Previous  8 Next   Top
    From: info@pap.com
    Date: Mon, 31 Mar 1997 13:15:16 -0500 (EST)
    Subject: PADD97

    PADD97 - The First International Conference and Exhibition on
    ====================================================
    The Practical Application of Knowledge Discovery and Data Mining
    =========================================================

    23rd April - 25th April 1997

    REGISTRATION
  • http://www.demon.co.uk/ar/Expo97/


    • INFORMATION
  • http://www.demon.co.uk/ar/PADD97/


    • TUTORIALS
    Usama Fayyad, Microsoft Research, USA
    Evangelos Simoudis, IBM, USA
    DATA Mining and the KDD Process
    Blaise Egan, Huw Roberts, BT Laboratories, UK
    Knowledge Discovery - Practical Methodology and Case Studies
    Luc De Raedt, Catholic University of Leuven, Belgium
    Principles and Practice of Inductive Logic Programming

    INVITED SPEAKERS
    Stephen Muggleton, Oxford University, UK
    Declarative Knowledge Discovery in Industrial Databases
    Usama Fayyad, Microsoft Research, USA
    Data Mining: Algorithms, Challenges and Limitations
    Xindong Wu, Monash University, Australia
    Building Intelligent Learning Database Systems
    Stephen Pass, Red Brick Systems, UK
    Data Mining and Data Warehouses - The Power of Integration
    Neil Mackin, White Cross Systems, UK
    The Application of WhiteCross MPP Servers to Data Mining


    PRACTICAL APPLICATION EXPO97
    ==============================
    CONFERENCE REGISTRATION
    =========================
    Westminster Central Hall, London, 21-25 April, 1997

    PADD97 is part of The Practical Application EXPO97 which brings together
    four events under one roof: PAAM97 - The Practical Application of
    Intelligent Agents and Multi-Agents; PADD97- The Practical Application of
    Knowledge Discovery and Data Mining; PACT97-The Practical Application of
    Constraint Technology and PAP97-The Practical Application of Prolog.

    REGISTRATION NOW AVAILABLE AT

  • http://www.demon.co.uk/ar/Expo97/



    • PLEASE VISIT OUR WEB PAGES FOR FURTHER INFORMATION ON

    Programmes
    Tutorials
    Invited Talks
    Exhibition
    Venue
    Hotel reservations

  • http://www.demon.co.uk/ar/PAP97/

  • http://www.demon.co.uk/ar/PACT97/

  • http://www.demon.co.uk/ar/PAAM97/

  • http://www.demon.co.uk/ar/PADD97/


    • The Practical Application Company
    PO Box 137
    Blackpool
    Lancs FY2 9UN
    UK
    Tel: +44 (0)1253 358081
    Fax: +44 (0)1253 353811
    email: info@pap.com
    WWW:
  • http://www.demon.co.uk/ar/TPAC/


    • Previous  9 Next   Top
    Date: Mon, 31 Mar 97 12:50:10 -0600 (CST)
    From: Melinda Conkling (melinda@springbok.com)
    Subject: Data warehousing event

    Hi -- The Data Warehousing Institute (www.dw-institute.com) is holding its
    Best Practices & Implementation Conference in Chicago May 27-June 1, 1997.
    All conference information (including how to register) can be found on-line.
    Thanks! -- Melinda
    Previous  10 Next   Top
    Date: Thu, 10 April Mar 1997 17:48:34 -0500
    From: Gregory Piatetsky-Shapiro (gps)
    Subject: Paris Data Mining'97 Event, June 2-4 -- cancelled

    I have been informed by Gaelle Piernikarch, organizer of the
    above conference, that it has been cancelled and
    may be rescheduled for fall.

    Previous  11 Next   Top