*
M. Smyth, Learning Methods Course by Hinton and Jordan,
Washington, D.C., May 2 -- 3, 1997
*
J. Zytkow, Forthcoming events related to Data Mining
PKDD'97, ISMIS-97 and KDD-97
--
Discovery in Databases (KDD) community, focusing on the latest research and
applications.
Submissions are most welcome and should be emailed,
with a DESCRIPTIVE subject line (and a URL, when available) to kdd@gte.com
To subscribe, email to kdd-request@gte.com
message with
subscribe kdd-nuggets
in the first line (the rest of the message and subject are ignored).
See http://info.gte.com/~kdd/subscribe.html
for details.
Nuggets frequency is approximately 3 times a month.
Back issues of Nuggets, a catalog of S*i*ftware (data mining tools),
and a wealth of other information on Data Mining and Knowledge Discovery
is available at Knowledge Discovery Mine site http://info.gte.com/~kdd
-- Gregory Piatetsky-Shapiro (editor)
********************* Official disclaimer ***********************************
* All opinions expressed herein are those of the writers (or the moderator) *
* and not necessarily of their respective employers (or GTE Laboratories) *
*****************************************************************************
~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
If there is a 50-50 chance that something can go wrong, then 9
times out of ten it will. (Paul Harvey News, 1979)
Excerpted from 'Quotes, damned quotes and...' by John Bibby. Previous1NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Mon, 3 Feb 1997 15:01:47 +0100
From: Willi.Kloesgen@gmd.de
(Willi Kloesgen)
Subject: KDD-97: Call for Panel Proposals
As in previous KDD conferences, the KDD-97 program will include panel
discussions. A great panel requires an interesting topic, good
speakers, and proper preparation. To facilitate all three we solicit
early suggestions. Please submit suggestions for topics and preferably also
for panelists who could represent diverse positions or approaches of the
topic. Suggested topics should relate to any of the main KDD-97 topics (see http://www-aig.jpl.nasa.gov/kdd97.
The panel topics should be of general interest for a
large part of the KDD audience and allow several (controversial) approaches
to be discussed.
Please email informal suggestions by April 2, 1997 (earlier if possible) to:
Willi Kloesgen
kloesgen@gmd.de
Previous2NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: 'Edward Colet'(ecolet@watson.ibm.com)
Date: Wed, 29 Jan 1997 18:00:02 -0400
Subject: Announcing a regular posting of NBA data mining patterns.
National Basketball Association teams have been using IBM's Advanced Scout
data mining application to discover trends and patterns in game data.
Now a selected set of discovered patterns are also made available to fans
via a regular posting on the Internet before and after NBA/NBC's game of
the week. The reported patterns are based on analyses of the teams
previous game(s), and additional commentary is added following the game.
*********************************************
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
********************************************* Previous3NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Mon, 3 Feb 1997 09:57:37 -0500
From: gps@gte.com
(Gregory Piatetsky-Shapiro)
Subject: Business Week Feb 3, 1997 Story on Data Mining
Last week's Business Week has a very nice story by John Verity on
'Coaxing Meaning out of Raw Data' (p. 134).
It described several successful customer modeling applications
at MCI, cellular fraud detection, US West, JPL, Walmart, and more
and featured quotes
from Usama Fayyad, Herb Edelstein, Steven Vere, and others.
'A huge opportunity is opening up', according to Usama,
but 'the devil really is in the details', according
to NeoVista CEO John Harte.
Can you please recommend the best PD and commercial data mining tool for
quantifying newgroups and email postings.
Thank you very much,
Brian Griffin
Manager, Technical Support
Netscape Communications Corp.
[GPS -- if you do know such tools, please cc to kdd@gte.com
and
I will summarize to the list] Previous5NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Wed, 29 Jan 1997 05:05:46 +0200
From: Michael Rebhan (lvrebhan@bioinformatics.weizmann.ac.il)
Organization: Weizmann Institute of Science
Subject: GeneCards: genes, proteins and diseases.
This database aims at integrating knowledge about all human genes, their
products, and their involvement in diseases. And although it already
integrates what is easily available in different heterogenous databases,
the authors are planning to use technology from Artificial Intelligence,
including Knowledge Discovery in Databases (KDD) tools, to expand the
current resource. We would like to hear opinions from people inside the
AI/KDD community regarding the following projects:
a) a user guidance system that recognizes problems caused by 'poorly
designed' search strategies entered to suggest intelligent options to
the user that might take him/her as fast as possible to the wanted
information (this system should thus somehow replace an expert in the
retrieval of biomedical information as much as possible).
b) knowledge extraction tools taking data from free text, like from
abstracts of papers in Medline, to gather data about the relationships
between genes/proteins (which one interacts directly with which one
a.s.o.), and about the role of a particular gene/protein in the
pathogenesis of a particular disease
Although both projects are still more or less ill-defined, we are very
interested in your ideas. If you are also fascinated by this challenge,
please email Michael Rebhan (lvrebhan@bioinformatics.weizmann.ac.il).
Michael Rebhan, Ph.D. Weizmann Institute of Science, Dept. Biol.
Serv.,
Bioinformatics Unit, Rehovot 76100, Israel (FAX: +972-8-934-4113)
WWW: http://bioinfo.weizmann.ac.il/cards/rebhan.html
Email: lvrebhan@bioinformatics.weizmann.ac.il
Previous6NextTop
>~~~Publications:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Mon, 27 Jan 1997 08:48:06 -0700
From: Amit Basu (basua@ctrvax.Vanderbilt.Edu)
Subject: cfp for INFORMS Journal on Computing
Call for Papers on Knowledge Discovery and Data Mining
for the INFORMS Journal on Computing
The knowledge and data management area of the INFORMS Journal on Computing
invites technical papers on the analysis, design and management of knowledge
discovery and data mining methods and systems. Selected papers will be
published in a special cluster on this topic. The journal is an official
publication of the Institute for Operations Research and Management
Sciences, and focuses on the interface between operations
research/management science and computer science. Papers that deal with
algorithms for system design, methods for efficient information management,
and analytical or empirical studies of system performance are welcome.
Topics of interest include (but are not limited to):
* performance analysis of KD/DM algorithms (efficiency, scalability,
reliability, etc.)
* the use of optimization methods in KD/DM
* comparative studies of KD/DM versus other exploratory data analysis
methods, including
traditional statistical and mathematical programming models
* analysis of context-specific KD/DM methods
* neural networks in KD/DM
* performance analysis of uncertainty management methods in KD/DM
* analysis of KD/DM algorithms in large-scale, distributed and/or
heterogeneous database systems
* efficiency and scalability analysis of KD/DM algorithms for specialized
databases
(spatial, temporal, multimedia, statistical, etc.)
* analysis of data mining methods on confidential data
* efficient data preprocessing methods (e.g., scrubbing, sampling and
reduction) for data mining
* performance of KD/DM methods on multidimensional data
Manuscripts should be prepared according to JoC guidelines.
Deadline: July 31, 1997. Four (4) copies of each manuscript should be
submitted to Professor Amit Basu, the Area Editor for Knowledge and Data
Management, at the following address:
Owen Graduate School of Management
Vanderbilt University
Nashville, TN 37203
TEL: 615-322-7043
FAX: 615-343-7177
email: basua@ctrvax.vanderbilt.edu
For more information, please contact Professor Basu at the above address, or
the Editor-in-Chief of JoC, Professor Bruce Golden, at the address below:
College of Business and Management
University of Maryland
College Park, MD 20742
TEL: 301-405-2232
FAX: 301-314-9157
email: bgolden@umdacc.umd.edu
------------------------------------------------------------------------------
Amit Basu
Associate Professor
Owen Graduate School of Management
Vanderbilt University
Nashville, TN 37203
TEL: 615-322-7043
FAX: 615-343-7177
Previous7NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Subject: IEEE Internet Computing: Agents
From: mpsingh@eos.ncsu.edu
(Munindar Singh)
Date: Wed, 29 Jan 1997 10:27:57 -0500 (EST)
IEEE Internet Computing is a new bimonthly magazine from the IEEE Computer
Society designed to help the engineer productively use the ever expanding
technologies and resources of the Internet. Internet Computing and IC on-line
will provide developers and users with the latest advances in Internet-based
computer applications and supporting technologies such as the World Wide Web,
Java programming, and Internet-based agents. Through the use of peer-reviewed
articles as well as essays, interviews, and roundtable discussions, IC will
address the Internet's widening impact on engineering practice and society.
IC is soliciting regular papers and papers for theme issues, including one on
agents. To submit, send e-mail to any member of the editorial board.
Include a plain text abstract, and a URL from which the paper can be viewed.
Agents: Editorial Board Contacts:
What kinds of agents are performing useful Munindar Singh
work on the Internet? Papers should singh@ncsu.edu
clearly define both the applications and or
technologies being used as well as the Michael Huhns
sense of 'agent.' Applications should be huhns@sc.edu
demonstrable. Issues include security, Due date: March 15, 1997
mobility, and agent communication
languages. Claims about the efficacy of
one approach or language should be
supported by examples from applications.
Previous8NextTop
>~~~Positions:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Sat, 25 Jan 1997 10:50:41 -0800
From: Wray Buntine (wray@Ultimode.com)
Subject: PhD/Masters Research Assistantship
PhD/Masters Research Assistantships
Field: probabilistic algorithms, data analysis/mining and
optimization for CAD
Place: Electrical Engineering and Computer Science
University of California, Berkeley
The CAD group in the EECS Dept. at UC Berkeley is offering research support
for its Masters and Doctoral program. Research areas include but are not
limited to the use of data mining/analysis/engineering techniques in CAD or
optimization, and probabilistic methods for optimization or specialized
compilation.
The Electronic Design Technology (EDT) field is concerned with computer
automated or computer-assisted design of complex electronic systems. With
current hardware capabilities advancing rapidly, a key bottleneck is the
development of advanced algorithms for optimization and simulation of
partial, abstract or completed designs. Our task is to design, code and
experiment with new algorithms, methodologies, and software technologies for
alleviating this bottleneck. The task can include the use of data
mining/analysis to understand the nature of the optimization task, or in
order to develop adaptive optimization methods.
The ideal candidate should have a background in computer science, electrical
engineering or related disciplines, should be an accomplished or developing
programmer, and should have an interest in the theory and mathematical
techniques used in optimization, data analysis, or probabilistic methods.
Candidates who wish to apply are invited to respond with a copy of their CV
to:
Description
Application of Machine Learning techniques to solve real-world problems
has gained more and more interest over the last decade. In spite of this
attention, the ML application process is still lacking a generally accepted
terminology, let alone commonly accepted approaches or solutions.
Several initiatives, both conferences and workshops have been held
concerning this topic.
The ICML-93 workshop of Langley and Kodratoff on ML applications as well
as at the ICML-95 workshop on 'Applying Machine Learning in Practice' by
Aha, Catlett, Hirsh and Riddle form the successful precedents of this workshop.
The focus of the ICML-95 workshop was the 'characterization of the
expertise used by machine learning experts during the course of applying
learning algorithms to practical applications'. In the last year a
significant research effort has been spent that deals with applications
of learning algorithms. A reflection of this is the recent interest in
Data Mining and KDD, as for instance reflected in the international KDD-
conference (1995 (Montreal) and 1996 (Portland, OR)). Since the
application of ML-techniques is also very relevant to the KDD-community
it is not surprising that this is also reflected in those conferences.
The workshop will draw along the lines of all these events, but
will emphasise the processes underlying the application of ML in
practice. Methodological issues, as well as issues concerning the kinds
and roles of knowledge needed for applying ML will form a major focus
of the workshop.
It aims at building upon some of the results of discussions at the
ICML-95 workshop on 'Application of ML techniques in practice'
and at the same time tries to move forward to a consensus regarding a
methodology on the application of learning algorithms in practice.
The workshop 'ML Application in the real world; methodological aspects and
implications' focuses on the methodological principles underlying
successful application of ML techniques. Apart from powerful ML
algorithms, good application strategies have to be defined. This implies a
thorough understanding of the initial problem definition and its relation
to the chain of tasks that leads towards a successful solution. Therefore a
two-dimensional approach regarding the process of ML application is
needed. The first dimension deals with the whole cycle of analysing the
setting, problem definition, knowledge extraction, database interaction,
learning, evaluation and iteration in real-world domains, where the second
dimension forms an 'inner loop' to this cycle, where the problem
definition is used to refine the task at hand and map it on available
algorithms for learning, pre- and postprocessing and evaluation of
results.
Concerning these issues there is no clear distinction between ML and KDD,
and therefore this workshop will be equally interesting for
researchers from both communities.
This workshop does not focus on (methods for) developing new algorithms.
Moreover, case studies will only contribute to the workshop discussion if
general application principles can be derived from them.
Intended Participants and Audience
The workshop primarily aims at scientists and practitioners that apply ML
and related techniques to solve problems in the real world. To attend
the workshop, one should submit a paper, a one page extended abstract or
a statement of interest. In case of too much interest from
participants, the program committee will select participants on the
basis of workshop relevance. Ideally, the audience contains a mix of
university and industrial participants.
Workshop program
The program for this one-day workshop will have a maximum of 10
presentations. Some invited presentations will be part of the program.
Presentations will take 30 minutes (15-20 minutes presentation and 10-15
minutes discussion). Speakers are asked to focus their presentation on
the basis of a topic list that will be compiled during the review
process. To foster discussion and debate, accepted papers will be given
to a critic beforehand; by these means critics will be prepared to
debate presentations. At the end of the workshop, there will be a
plenary discussion session. Accepted papers will be distributed via the
workshop WWW-page before the workshop, to stimulate the discussion.
Accepted papers will also be published in workshop proceedings.
Papers are welcomed concerning (but not limited to) the following
topics:
* Methodological approaches focusing on the process of ML application,
or sub-processes, such as problem definition and refinement,
application design, data acquisition, pre- and postprocessing, task
analysis etc.
* Making explicit the kinds and roles of knowledge that are necessary
for execution of ML applications.
* Matching of problem definitions on specific techniques and multi-
technique configurations.
* Impact of methodologies for empirical research on the application of
ML-techniques.
* Identification of the relation of different ML strategies to given
problem types and identification of the characteristics that play a
role in describing the initial problems.
* Embedding of the ML application process in more general methodologies
for (knowledge) system development.
* Frameworks for support of (ML-)novices and experts for setting up
applications and reuse of previously application(part)s.
* Case studies, describing successful ML applications, that abstract
from the implementational aspects and focus on identification of the
choices that are made when designing the application i.e. the
(meta-)knowledge involved, etc.
* Comparison of the process of ML application with processes for
application of related techniques (e.g. statistical data analysis).
Submission guidelines
* Submitted papers should not exceed 3500 words or 8 pages Times Roman
12pt.
* The title page should contain paper title, author name(s), affiliations and
full addresses including e-mail of the corresponding author, as well as the
paper abstract and five keywords at most.
* Papers are reviewed by at least three members of the program committee on
their relevance for the workshop discussions.
* For preparation of the camera ready copies, an ICML style file will be
available.
Tentative Submission Schedule
* Submission deadline: March 22, 1997
* Notification of acceptance: April 9, 1997
* Camera ready copy + PS-file: May 1, 1997
* Papers available on WWW: June 15, 1997
* Workshop date: July 12, 1997
Electronic paper submissions are preferred. Please send your submission
to:
MLApplic.ICML@ato.dlo.nl.
If Postscript printing is not available, paper submissions (4 hardcopies,
preferably double sided) can be sent to:
ICML Workshop 'ML APPLICATION IN THE REAL WORLD'
p/o ATO-DLO, Floor Verdenius
Postbus 17
6700 AA Wageningen
Netherlands
Program Committee
Dr. Pieter Adriaans (Syllogic, Houten, The Netherlands)
Prof. C. Brodley (Purdue University, West Lafayette, IND, USA)
Prof. David Hand (Open University, Milton Keynes, United Kingdom)
Prof. Yves Kodratoff (LRI, Paris, France)
Dr. Vassilis Moustakis (Technical University of Crete, Chania, Greece)
Prof. Gholamreza Nakhaeizadeh (Daimler Benz AG Research, Ulm, Germany)
Dr. R. Kohavi (Silicon Graphics, Mountain View, CA, USA)
Dr. Enric Plaza i Cervera (IIIA-CSIC, Bellaterra, Catalonia, Spain)
Dr. Foster J. Provost (NYNEX Science & Technology, White Plains, NY,
USA)
Dr. P. Riddle (University of Auckland, New Zealand)
Dr. Celine Rouveirol (LRI, Paris, France)
Prof. Derek Sleeman (University of Aberdeen, United Kingdom)
Drs. Maarten van Someren (SWI, Amsterdam, The Netherlands)
Prof. Rudi Studer (University of Karlsruhe, Germany)
Organising Committee
Robert Engels (University of Karlsruhe, Germany)
engels@aifb.uni-karlsruhe.de
Juergen Herrmann (University of Dortmund, Germany)
Herrmann@jupiter.informatik.uni-dortmund.de
Bob Evans (RR Donnelley, Gallatin TN, USA)
BOB.EVANS@rrd.com
Floor Verdenius (ATO-DLO, Wageningen, The Netherlands)
F.Verdenius@ato.dlo.nl
Previous10NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Marney Smyth (marney@ai.mit.edu)
Subject: Learning Methods Tutorial -- Washington DC, May 1997
Date: Sat, 1 Feb 1997 12:19:02 -0500 (EST)
**************************************************************
*** ***
*** Learning Methods for Prediction, Classification, ***
*** Novelty Detection and Time Series Analysis ***
*** ***
*** Washington, D.C., May 2 -- 3, 1997 ***
*** ***
*** Geoffrey Hinton, University of Toronto ***
*** Michael Jordan, Massachusetts Inst. of Tech. ***
*** ***
**************************************************************
A two-day intensive Tutorial on Advanced Learning Methods will be held
on May 2nd and 3rd, 1997, at the Hyatt Regency on Capitol Hill,
Washington D.C. Space is available for up to 50 participants for the
course.
The course will provide an in-depth discussion of the large collection
of new tools that have become available in recent years for developing
autonomous learning systems and for aiding in the analysis of complex
multivariate data. These tools include neural networks, hidden Markov
models, belief networks, decision trees, memory-based methods, as well
as increasingly sophisticated combinations of these architectures.
Applications include prediction, classification, fault detection,
time series analysis, diagnosis, optimization, system identification
and control, exploratory data analysis and many other problems in
statistics, machine learning and data mining.
The course will be devoted equally to the conceptual foundations of
recent developments in machine learning and to the deployment of these
tools in applied settings. Case studies will be described to show how
learning systems can be developed in real-world settings. Architectures
and algorithms will be presented in some detail, but with a minimum of
mathematical formalism and with a focus on intuitive understanding.
Emphasis will be placed on using machine methods as tools that can
be combined to solve the problem at hand.
WHO SHOULD ATTEND THIS COURSE?
The course is intended for engineers, data analysts, scientists,
managers and others who would like to understand the basic principles
underlying learning systems. The focus will be on neural network models
and related graphical models such as mixture models, hidden Markov
models, Kalman filters and belief networks. No previous exposure to
machine learning algorithms is necessary although a degree in engineering
or science (or equivalent experience) is desirable. Those attending
can expect to gain an understanding of the current state-of-the-art
in machine learning and be in a position to make informed decisions
about whether this technology is relevant to specific problems in
their area of interest.
COURSE OUTLINE
Overview of learning systems; LMS, perceptrons and support vectors;
generalized linear models; multilayer networks; recurrent networks;
weight decay, regularization and committees; optimization methods;
active learning; applications to prediction, classification and control
Graphical models: Markov random fields and Bayesian belief networks;
junction trees and probabilistic message passing; calculating most
probable configurations; Boltzmann machines; influence diagrams;
structure learning algorithms; applications to diagnosis, density
estimation, novelty detection and sensitivity analysis
Clustering; mixture models; mixtures of experts models; the EM
algorithm; decision trees; hidden Markov models; variations on
hidden Markov models; applications to prediction, classification
and time series modeling
Subspace methods; mixtures of principal component modules; factor
analysis and its relation to PCA; Kalman filtering; switching
mixtures of Kalman filters; tree-structured Kalman filters;
applications to novelty detection and system identification
Approximate methods: sampling methods, variational methods;
graphical models with sigmoid units and noisy-OR units; factorial
HMMs; the Helmholtz machine; computationally efficient upper
and lower bounds for graphical models
REGISTRATION
Standard Registration: $700
Student Registration: $400
Cancellation Policy: Cancellation before Friday April 25th, 1997,
incurs a penalty of $150.00. Cancellation after Friday April 25th,
1997, incurs a penalty of one-half of Registration Fee.
Registration Fee includes Course Materials, breakfast, coffee breaks,
and lunch.
On-site Registration is possible. Payment of on-site registration must
be in US Dollar amounts, by Money Order or Check (preferably drawn on
a US Bank account).
Those interested in participating should return the completed
Registration Form and Fee as soon as possible, as the total number of
places is limited by the size of the venue.
Please print this form, and fill in the hard copy to return by mail
REGISTRATION FORM
Learning Methods for Prediction, Classification,
Novelty Detection and Time Series Analysis
Friday, May 2 - Saturday, May 3, 1997
Washington, D.C., USA.
--------------------------------------
Please complete this form (type or print)
Name ___________________________________________________
Last First Middle
Firm or Institution ______________________________________
Standard Registration ____ Student Registration ____
Fee payment must be made by MONEY ORDER or PERSONAL CHECK. All amounts
are given in US dollar figures. Make fee payable to Prof. Michael
Jordan. Mail it, together with this completed Registration Form to:
Professor Michael Jordan
Dept. of Brain and Cognitive Sciences
M.I.T.
E10-034D
77 Massachusetts Avenue
Cambridge, MA 02139
USA
HOTEL ACCOMMODATION
Hotel accomodation is the personal responsibility of each participant.
The Tutorial will be held in
Hyatt Regency on Capitol Hill
400 New Jersey Avenue, NW
Washington, DC 20001
1-800-233-1234 or (202) 737-1234
on May 2 -- 3, 1997.
The hotel has reserved a block of rooms for participants of the course. The
special room rates for participants are:
U.S. $139.00 (Single/Double) per night + tax
You must reserve accommodation before *April 1, 1997* to avail of this
special rate. Please be aware that these prices do not include State
or City taxes.
ADDITIONAL INFORMATION
A registration form is available from the course's WWW page at
Previous11NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Mon, 3 Feb 1997 22:47:43 -0600
From: jan zytkow (zytkow@cs.twsu.edu)
Dear Colleague:
You may be interested in the following forthcoming events related to
machine discovery. Please notice that there is still time to submit a
paper to each of these events:
1. PKDD'97 -- 1st European Symposium on Principles of Data Mining
and Knowledge Discovery, Trondheim, Norway, June 25-27, 1997
Deadline for submissions: February 17
2. International Symposium on Methodologies for Intelligent Systems
(ISMIS-97), Charlotte, North Carolina, October 15-18, 1997
Machine discovery and learning is a strong theme at ISMIS
Deadline for submissions: March 1.
3. The Third International Conference on Knowledge Discovery and Data
Mining (KDD-97), Newport Beach, California, August 14-17, 1997
Deadline for submissions: March 10 (Cover page by March 3).
The original deadline for submitting papers to the 1997 Principles of
Knowledge Discovery in Databases was Wednesday, February 5. This
deadline has been extended, so that PKDD-97 papers are now due on
Monday, February 17, 1997
Notice of acceptance: March 17
Camera ready copies: April 4
Submit by email (preferred) to pkdd97@idt.ntnu.no
or by airmail to
Jan Komorowski
Department of Computer Systems
Norwegian University of Science and Technology
7034 Trondheim, Norway
Papers should be in English and not exceed ten single-spaced pages of
12pt font. The first page should begin with title, authors,
affiliations, surface and e-mail addresses, and an abstract of about
200 words.
The proceedings of the Symposium will be published in the Springer
Verlag Lecture Notes AI Series (www.springer.de/comp/comp.html) and
available at PKDD-97, June 25-27.
PKDD'97 -- 1st European Symposium on Principles of
Data Mining and Knowledge Discovery
Trondheim, Norway
June 25-27, 1997
Program Committee Introduction
* Pieter Adriaans Data Mining and Knowledge Discovery (KDD)
* Attilio Giordana have recently emerged from a combination of
* David Hand many research areas: databases, statistics,
* Bob Henery machine learning, automated scientific
* Mikhail Kiselev discovery, inductive programming, artificial
* Willi Kloesgen intelligence, visualization, decision
* Yves Kodratoff science, and high performance computing.
* Jan Komorowski
* Heikki Manilla While each of these areas can contribute in
* Marjorie Moulet specific ways, KDD focuses on the value that
* Steve Muggleton is added by creative combination of the
* Zdzislaw Pawlak contributing areas. The goal of PKDD'97 is
* Gregory to provide a European-based forum for
Piatetsky-Shapiro interaction among all theoreticians and
* Zbigniew Ras practitioners interested in data mining.
* Lorenza Saitta Fostering an interdisciplinary collaboration
* Erik Sandewall is one desired outcome, but the main
* Wei-Min Shen long-term focus is on theoretical principles
* Arno Siebes for the emerging discipline of KDD,
* Andrzej Skowron especially those new principles that go
* Derek Sleeman beyond each of the contributing areas.
* Shusaku Tsumoto
* Raul Valdes-Perez To promote these goals, PKDD'97 will be
* Rudiger Wirth organized into tracks around the key areas
* Stefan Wrobel contributing to KDD. For each area an ideal
* Wojtek Ziarko paper should focus on how its methods
* Jan Zytkow advance KDD's goals and principles.
Both theoretical and applied submissions are
sought. Reviewers will assess the
contribution towards the main goals of
PKDD'97, in addition to the usual
requirements of novelty, clarity and
significance. Applied papers should go
beyond an individual application, presenting
an explicit method that promises a degree of
generality within some stage of the
discovery process, such as preprocessing,
mining, visualization, use of prior
knowledge, knowledge refinement, and
evaluation. Theoretical papers should
demonstrate how they advance the process of
data mining and knowledge discovery.
------------------------------------------------------------------
2.
------------------------------------------------------------------
**** C A L L F O R P A P E R S ****
TENTH INTERNATIONAL SYMPOSIUM ON
METHODOLOGIES FOR INTELLIGENT SYSTEMS (ISMIS'97)
Hilton Hotel, Charlotte, North Carolina
October 15-18, 1997
SPONSORS
UNC-Charlotte, Oak Ridge National Laboratory, Univ. of Warsaw, and others.
PURPOSE OF THE SYMPOSIUM
This Symposium is intended to attract individuals who are actively
engaged both in theoretical and practical aspects of intelligent systems.
The goal is to provide a platform for a useful exchange between
theoreticians and practitioners, and to foster the cross-fertilization
of ideas in the following areas:
* Evolutionary Computation
* Intelligent Information Systems
* Learning and Knowledge Discovery
* Knowledge Representation and Integration
* Logic for Artificial Intelligence
* Robotics, Motion and Machine Vision
* Soft Computing
* Methodologies (modeling, design, validation, performance evaluation).
In addition, we solicit papers dealing with Applications of Intelligent
Systems in complex/novel domains, e.g. human genome, global change,
manufacturing, health care, etc.
SYMPOSIUM CHAIRS
Francois G. Pin (Oak Ridge National Lab.)
Zbigniew W. Ras (UNC-Charlotte & Polish Acad. Sci.)
Andrzej Skowron (U. Warsaw, Poland)
PROGRAM COMMITTEE
Luigia Carlucci Aiello (U. Roma, Italy)
Thomas Baeck (Inf. Centrum Dortmund & U. Leiden, The Netherlands)
Alan Biermann (Duke Univ.)
Jacques Calmet (U. Karlsruhe, Germany)
Jaime Carbonell (CMU)
Wesley Chu (UCLA)
Kenneth DeJong (GMU)
Robert Demolombe (CERT/ONERA, France)
Jon Doyle (MIT)
Toshio Fukuda (Nagoya U., Japan)
Attilio Giordana (U. Torino, Italy)
Diana Gordon (Naval Research Lab.)
Mirsad Hadzikadic (Carolinas HealthCare System)
Jiawei Han (Simon Fraser U., Canada)
David Hislop (Army Research Office)
Matthias Jarke (RWTH Aachen, Germany)
John Y. Jiang (Pacific Bell Lab.)
Willi Kloesgen (GMD, Germany)
Yves Kodratoff (U. Paris VI, France)
Jan Komorowski (U. Trondheim, Norway)
Alberto Martelli (U. Torino, Italy)
Robert Meersman (U. Brussels, Belgium)
Zbigniew Michalewicz (UNC-Charlotte & Polish Acad. Sci.)
Ryszard Michalski (GMU & Polish Acad. Sci.)
Jack Minker (U. Maryland)
Ephraim Nissan (U. Greenwich, UK)
Lin Padgham (RMIT U., Australia)
Rohit Parikh (CUNY)
Lynne Parker (ORNL)
Gregory Piatetsky-Shapiro (GTE Lab.)
Henri Prade (U. Paul Sabatier, France)
Luc De Raedt (U. Leuven, Belgium)
Marek Rusinkiewicz (MCC)
Lorenza Saitta (U. Torino, Italy)
Erik Sandewall (Linkoping U., Sweden)
Yoav Shoham (Stanford U.)
Richmond Thomason (U. Pittsburgh)
Jing Xiao (UNCC)
Carlo Zaniolo (UCLA)
Gian Piero Zarri (CNRS, France)
Maria Zemankova (NSF)
Jan M. Zytkow (Wichita State U. & Polish Acad. Sci.)
INVITED TALKS
Alan Biermann (Duke Univ.)
'Multimedia Dialogue: Theory and Practice'
Jaime Carbonell (CMU)
'Automated Text Summarization' or 'Learning from the WEB'
Wesley Chu (UCLA)
'A knowledge-based multimedia medical distributed database system'
Michael Lowry (NASA Ames)
'V&V of AI systems that control deep-space spacecraft'
Gregory Piatetsky-Shapiro (GTE Lab.)
'Data Mining and Knowledge Discovery: The Second Generation'
Gio Wiederhold (Stanford U.)
'Achieving scalibility through an Ontology Algebra'
ORGANIZING COMMITTEE
Brian Bachman (First Union)
Mirsad Hadzikadic (Carolinas HealthCare System)
Karen Harber (ORNL)
Mieczyslaw Klopotek (Polish Acad. Sci.)
M.S. Narasimha (IBM-Charlotte)
Zbigniew W. Ras (UNC-Charlotte)
PAPER SUBMISSION
Authors are invited to submit four copies of their manuscript
(maximum 12 pages) to one of the addresses below:
Papers from US and Canada: Papers from Europe:
Francois G. Pin, ISMIS'97 Andrzej Skowron, ISMIS'97
ORNL, Bldg. 7601, M.S. 6305 Univ. of Warsaw
P.O. Box 2008 Dept. of Mathematics
Oak Ridge, TN 37831-6305 Banacha 2
e-mail: pin@ORNL.GOV
PL-02-097 Warsaw, POLAND
fax: 423-574-4624 e-mail: skowron@mimuw.edu.pl
tel: 423-574-6130 tel: 48-(22)-658-3449
All other papers:
Zbigniew W. Ras, ISMIS'97
Univ. of North Carolina
Dept. of Comp. Science
Charlotte, N.C. 28223
e-mail: ras@uncc.edu
fax: 704-547-3516
tel: 704-547-4567
Submissions should include a title page (1 copy) specifying the
title, all authors with their affiliations, abstract (100-200 words),
up to 10 keywords (begin the keyword list with at least one of the
ISMIS areas listed above); and the preferred address of the contact
author, including a telephone number, fax number, and e-mail address
(if available). The remainder of the paper can include up to 11 pages,
attached to the title page.
If possible, the title page should be ADDITIONALLY submitted via email
(in plain text) to (ras@uncc.edu)
to facilitate submissions processing.
IMPORTANT DATES
Submission of Papers: March 1, 1997
Acceptance Notification: May 25, 1997
Final Paper: July 1, 1997
PUBLICATION
Papers accepted for Regular Sessions will be published by
Springer-Verlag in LNCS/LNAI.
Poster Session proceedings will be published by Oak Ridge
National Laboratory.
Both proceedings will be available at the symposium.
The Third International Conference on
Knowledge Discovery and Data Mining (KDD-97)
August 14-17, 1997
Newport Beach, California, U.S.A.
Sponsored by the American Association for Artificial Intelligence
----------------------------------------------------------------------------
Call for Papers
The rapid growth of data and information has created a need and
an opportunity for extracting knowledge from databases, and both
researchers and application developers have been responding to that need.
Knowledge discovery in databases (KDD), also referred to as data mining, is
an area of common interest to researchers in machine discovery, statistics,
databases, knowledge acquisition, machine learning, data visualization, high
performance computing, and knowledge-based systems. KDD applications have
been developed for astronomy, biology, finance, insurance, marketing,
medicine, and many other fields.
The third international conference on knowledge discovery and
data mining (KDD-97) will follow up the success of KDD-95 and KDD-96
by bringing together researchers and application developers from
different areas focusing on unifying themes.
Suggested Topics
The topics of interest include, but are not limited to:
Theory and Foundational Issues in KDD
* Data and knowledge representation for KDD
* Probabilistic modeling and uncertainty management in KDD
* Modeling of structured, unstructured and multimedia data
* Fundamental advances in search, retrieval, and discovery methods
* Definitions, formalisms, and theoretical issues in KDD
Data Mining Methods and Algorithms
* Algorithmic complexity, efficiency and scalability issues in data
mining
* Probabilistic and statistical models and methods
* Using prior domain knowledge and re-use of discovered knowledge
* Parallel and distributed data mining techniques
* High dimensional datasets and data preprocessing
* Unsupervised discovery and predictive modeling
KDD Process and Human Interaction
* Models of the KDD process
* Methods for evaluating subjective relevance and utility
* Data and knowledge visualization
* Interactive data exploration and discovery
* Privacy and security
Applications
* Data mining systems and data mining tools
* Application of KDD in business, science, medicine and engineering
* Application of KDD methods for mining knowledge in text, image, audio,
sensor, numeric, categorical or mixed format data
* Resource and knowledge discovery using the Internet
This list of topics is not intended to be exhaustive but an indication of
typical topics of interest. Prospective authors are encouraged to submit
papers on any topics of relevance to knowledge discovery and data mining.
Demonstration Sessions
KDD-97 also invites working demonstrations of discovery systems.
Contact information for details is provided below.
Submission and Review Criteria
Both research and applications papers are solicited. All submitted papers
will be reviewed on the basis of technical quality, relevance to KDD,
novelty, significance, and clarity. Authors are encouraged to make their
work accessible to readers from other disciplines by including a carefully
written introduction. Papers should clearly state their relevance to KDD.
Please submit 7 hardcopies of a short paper (a maximum of 9
single-spaced pages not including cover page and bibliography, 1 inch
margins, and 12pt font) to be received by March 10, 1997. A cover
page must include author(s) full address, email, paper title and a 200
word abstract, and up to 5 keywords. This cover page must accompany
the paper. In addition, an ascii version of the cover page must be
submitted electronically by March 3, 1997 (earlier if possible),
preferably using a WWW form located at http://www-aig.jpl.nasa.gov/kdd97/.
If the WWW form cannot be used,
please submit the ascii cover page by email to
kdd97pgm@aig.jpl.nasa.gov,
using the template available by ftp at http://www-aig.jpl.nasa.gov/kdd97/.
Please mail the 7 hardcopies of the full papers to:
AAAI (KDD-97)
445 Burgess Drive
Menlo Park, CA 94025-3496 USA
Phone: (+1 415) 328-3123
Fax: (+1 415) 321-4457
Email: kdd@aaai.org
Web Site: http://www.aaai.org.
Important Dates
* Submissions Due: March 10, 1997
* Acceptance Notice: April 28, 1997
* Camera-ready paper due: May 26, 1997
KDD-97 Organization
-------------------
General Conference Chair
Ramasamy Uthurusamy (General Motors Corporation, USA)
Program Co-Chairs
David Heckerman (Microsoft Research, USA)
Heikki Mannila (University of Helsinki, Finland)
Daryl Pregibon (AT&T Research, USA)
Publicity Chair
Paul Stolorz (Jet Propulsion Laboratory, USA)
Tutorial Chair
Padhraic Smyth (UC Irvine, USA)
Demo and Poster Sessions Chair
Tej Anand (NCR Corporation, USA)
Awards Chair
Gregory Piatetsky-Shapiro (GTE Laboratories, USA)
Panel Chair
Willi Kloesgen
Contact Information
-------------------
For further information, send inquiries regarding
* submission logistics to AAAI at kdd@aaai.org
Phone: (+1 415) 328-3123
Fax: (+1 415) 321-4457
* KDD-97 sponsorship and industry participation to
Ramasamy Uthurusamy samy@gmr.com
Phone: 810-696-0669
Fax: 810-696-0580
* technical program and content to kdd97pgm@aig.jpl.nasa.gov
* demo and poster sessions to tanand@winhitc.atlantaga.ncr.com
* general and publicity issues to kdd97@aig.jpl.nasa.gov
Knowledge Discovery Nuggets 97:05