--
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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
(Internet Humor -- thanks to Sarah Hedberg and others)
The chicken crossing the road...
NT Chicken:
Will cross the road in June. No, August. September for sure.
OS/2 Chicken:
It crossed the road in style years ago, but it was so quiet
that nobody noticed.
Win 95 Chicken:
You see different colored feathers while it crosses, but cook
it and it still tastes like ... chicken.
Microsoft Chicken (TM):
It's already on both sides of the road. And it just bought the
road.
OOP Chicken:
It doesn't need to cross the road, it just sends a message.
Assembler Chicken:
First it builds the road ...
C Chicken:
It crosses the road without looking both ways.
C++ Chicken:
The chicken wouldn't have to cross the road, you'd simply refer
to him on the other side.
Ada Chicken:
The chicken didn't cross the road - it was a wrong type (of chicken)
VB Chicken:
USHighways!TheRoad.cross (aChicken)
Delphi Chicken:
The chicken is dragged across the road and dropped on the other
side.
Java Chicken:
If your road needs to be crossed by a chicken, the server will
download one to the other side. (Of course, those are chicklets)
Web Chicken:
Jumps out onto the road, turns right, and just keeps on running.
Previous1NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: samy@sparc5a.ilab.c4.gmeds.com
Date: Fri, 6 Dec 1996 09:01:58 -0500
Subject: KDD97 CFP and Program Committee
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
* Probabilistic/statistical modeling and uncertainty management
* Data and knowledge representation
* Modeling of structured, unstructured and multimedia data
* Fundamental advances in search, retrieval, and discovery methods
Data Mining Methods and Algorithms
* Probabilistic and statistical models and methods
* Algorithmic complexity, efficiency and scalability issues in data
mining
* Using prior domain knowledge and re-use of discovered knowledge
* Data mining techniques implemented on scalable platforms, including
parallel, distributed and clustered systems
* 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),
preferrably 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 Labs, 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 (GMD, Germany)
Program Committee
-----------------
Tej Anand (NCR, USA)
Ron Brachman (AT&T Laboratories, USA)
Carla Brodley (Purdue University, USA)
Dan Carr (George Mason University. USA)
Peter Cheeseman (NASA AMES Research Center, USA)
David Cheung (University of Hong Kong, Hong Kong)
Wesley Chu (University of California at Los Angeles, USA)
Gregory Cooper (University of Pittsburgh, USA)
Robert Cowell (City University, UK)
Bruce Croft (University of Massachusetts at Amherst, USA)
Bill Eddy (Carnegie Mellon University, USA)
Charles Elkan (Univeristy of California at San Diego, USA)
Usama Fayyad (Microsoft Research, USA)
Ronen Feldman (Bar-Ilan University, Israel)
Jerry Friedman (Stanford University, USA)
Dan Geiger (Technion, Israel)
Clark Glymour (Carnegie-Mellon University, USA)
Moises Goldszmidt (Stanford Research Institute, USA)
George Grinstein (University of Lowell, USA)
Jiawei Han (Simon Fraser University, Canada)
David Hand (Open University, UK)
David Heckerman (Microsoft Corporation, USA)
Haym Hirsh (Rutgers University, USA)
Jim Hodges (University of Minnesota, USA)
Se June Hong (IBM T.J. Watson Research Center, USA)
Tomasz Imielinski (Rutgers University, USA)
Yannis Ioannidis (University of Wisconsin, USA)
Larry Jackel (AT&T Laboratories, USA)
David Jensen (Univsersity of Massachusetts, USA)
Michael Jordan (Massachusetts Institute of Technology, USA)
Dan Keim (University of Munich, Germany)
Willi Kloesgen (GMD, Germany)
Ronny Kohavi (Silicon Graphics, USA)
David Madigan (University of Washington, USA)
Heikki Mannila (University of Helsinki, Finland)
Brij Masand (GTE Laboratories, USA)
Gary McDonald (General Motors Research, USA)
Eric Mjolsness (University of California at San Diego, USA)
Sally Morton (Rand Corporation, USA)
Richard Muntz (University of California at Los Angeles, USA)
Raymond Ng (University of British Columbia, Canada)
Steve Omohundro (NEC Research, USA)
Gregory Piatetsky-Shapiro (GTE Laboratories, USA)
Daryl Pregibon (Bell Laboratories, USA)
Pat Riddle (Boeing Computer Services, USA)
Jude Shavlik (University of Wisconsin at Madison, USA)
Wei-Min Shen (University of Southern California, USA)
Arno Siebes (CWI, Netherlands)
Avi Silberschatz (Bell Laboratories, USA)
Evangelos Simoudis (IBM Almaden Research Center, USA)
Andrzej Skowron (University of Warsaw, Poland)
Padhraic Smyth (University of California at Irvine, USA)
Ramakrishnan Srikant (IBM Almaden Research Center, USA)
John Stasko (Georgia Institute of Technology, USA)
Sal Stolfo (Columbia University, USA)
Paul Stolorz (Jet Propulsion Laboratory, USA)
Alex Tuzhilin (NYU Stern School, USA)
Ramasamy Uthurusamy (General Motors R&D Center, USA)
Graham Wills (Bell Laboratories, USA)
David Wolpert (IBM Almaden Research Center, USA)
Wojciech Ziarko (University of Regina, Canada)
Jan Zytkow (Wichita State University, USA)
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
Previous2NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: HOSKING@watson.ibm.com
Date: Mon, 9 Dec 96 10:13:40 EST
Subject: first mention of data mining
The earliest mention of data mining that I have found is from 1983:
the paper 'Data mining' by Michael C. Lovell, Review of Economics and
Statistics, vol. 65, pp. 1-12. It uses 'data mining' in the pejorative
sense common among statisticians and econometricians.
'Data Mining', a new book by Peter Adriaans and Dolf Zantinge
was recently published. This is a slim volume that gives a nice,
business-oriented summary of the issues.
This book offers a comprehensive introduction to data mining, aiming
to provide essential insights and guidelines to help you make the
right decisions when setting up a data mining environment. It offers
clear answers to questions such as: --What is data mining? --Which
techniques are suitable for my data? --How do I set up a data mining
environment? --How do I justify the cost? The whole data mining
process, including data selection, cleaning, coding, different pattern
recognition techniques and reporting, is illustrated by means of an
extensive case study and numerous examples. Previous4NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Leo Breiman (leo@stat.Berkeley.EDU)
Date: Sat, 7 Dec 1996 21:29:34 -0800
PASTING BITES TOGETHER FOR PREDICTION IN LARGE DATA SETS
AND ON-LINE
Leo Breiman
Statistics Department
University of California
Berkeley, CA. 94708
leo@stat.berkeley.edu
Abstract
The size of many data bases have grown to the point where they
cannot fit into the fast memory of even large memory machines, to
say nothing of current workstations. If what we want to do is to use
these data bases to construct predictions of various characteristics,
then since the usual methods require that all data be held in fast
memory, various work-arounds have to be used. This paper studies
one such class of methods which give accuracy comparable to that
which could have been obtained if all data could have been held in
core and which are computationally fast. The procedure takes
small bites of the data, grows a predictor on each small bite and
then pastes these predictors together. The methods are also
applicable to on-line learning.
Previous5NextTop
>~~~Siftware:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Fri, 06 Dec 1996 19:12:10 +0100
From: 'D.A. Zighed' (zighed@univ-lyon2.fr)
Subject: SIPINA-W (latest version Dec. 1996)
Dear Gregory,
I'm pleased to announce you that SIPINA-W V2.0 (freeware version) and a
professional version SIPINA-PRO are ready. Would you please insert the
following contribution in the next publication of kdd nuggets. I have
enclosed the new update of SIPINA Web-Page.
Thank you very much
Best regards
Prof. D.A., ZIGHED,
E.R.I.C._Lyon, Bat. L, Universite Lumiere Lyon 2,
5 avenue Pierre Mendes-France
C.P.11 F69676 Bron Cedex,France
Tel./Fax (33) 4 78 77 23 76
e-mail : zighed@univ-Lyon2.fr
WEB : http://eric.univ-lyon2.fr/eric.html
------------------------------------------------------------------------
---------------------------------------------------------------/
1.) Announcement SIPINA-W V2.0 and SIPINA-PRO /
-------------------------------------------------------------/
The laboratory ERIC_LYON (Equipe de Recherche en Ingenierie des
Connaissances) is pleased to announce the new version of the platform
SIPINA_W (v2.0) containing new functions and new tools for Data Mining and
Knowledge Discovery.
You may already load it directly from the
ERIC_LYON server by an anonymous FTP at the following address: ftp://eric.univ-lyon2.fr/pub/sipina
or from Simtel sites (in few days).
-----------
This version contains several methods of induction graphs and some tools to
evaluate knowledge based system.
a - DATA MANAGER Module [NEW]
=============================
SIPINA_W has a data Manager module that allows you to import, export and
manipulate dataset (feature construction,discretization of continuous
attributes,data
visualization). You may create synthetized variable by applying mathematical
transformation.
Data Manager includes four types of graphs (scatter plot,values
distribution,...).
files format accepted by DataManager are:
WKS (Lotus),DBF(dBase),DB (Paradox),DAT (UCI Irvine Repository).
b - ANALYSIS Module
===================
Several methods are implemented:
- CART [Breiman & al. 1984]: complete program proposing two criteria
(Twoing Rule, Gini index), as well as the pruning algorithm;
- Elisee [Bouroche & Tenenhaus 1970]: binary segmentation method using
the Chi-2 criterion;
- ID3 [Quinlan 1979/1986];
- C4.5 [Quinlan 1992]: includes the pruning and the simplification of rules;
- ChAID (Chi2-Link) [Kass 1974]
- SIPINA [ZIGHED 1985/1992]: generalisation of trees by induction
graphs,including the discretisation methods seen above.
and 2 new induction methods
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- 1.) QR_MDL [Quinlan & Rivest 1989] : Induction tree with MDL principle.
- 2.) WDTaiqm [Wehenkel 1993] : Induction tree using an additive
informational quality measure, based on a bayesian approach.
c - Tests and Evaluation
========================
To evaluate induction performances, you have several strategy :
- 1.) You may divide the data file into a learning sample and a test
sample. The learning is automaticaly performed followed by the
validation on the second sample.
- 2.)You may also activate a cross-validation where the draw of a
sub-sample may be either randomly or stratified.
- 3.)You can also use a bootstrap procedure
and 1 new validation method
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- 4.)You can perform multiple trial to compare performances of two or more
induction methods. Dataset are automatically divided into learning and test
sample with a user's fixed ratio
d - Automatic / Interactive Learning Module
===========================================
When using the automatic learning procedure you only have to choose the
method and execute the analysis. The interactive learning mode enables you
to force the operations to be executed (Split, Merge), as well as the
variables used (surrogate split) on each vertex. The vertex inspection makes
possible to visualise the available information on the selected vertex:
distribution of the classes, observations list,distribution function on each
variable, discrimination level of each variable on competing splits.
e - Advanced manipulations of rules
===================================
e.1. Extracting rules
- Rules issued from a learning process are saved in different bases.
- Rules can be saved in production rule or PROLOG format,
- From each non-initial vertex it is now possible to produce prediction
rules which can be evaluated through their error rate, their corresponding
number of observations and an implication test based on the Lerman statistic
[Lerman 1981, Intensity of Implication].
e.2. Rules bases manipulation
You can
- Merge several rules bases;
- Insert rules manually and evaluate them by means of the data set.
e.3. Selection of the best rules by validation
During the application step of a rules base on a test or generalisation
sample the specification of the selection criterion for the competing rules
may be altered (an individual may respond to two rules, both having
different conclusions; this is
mostly possible when executing a merge of rules bases). The criteria are:
- minimisation of the error rate,
- maximisation of a rule's number of individuals,
- maximisation of the Goodman index [1988],
- maximisation of the intensity of implication (Lerman 1981).
e.4. Optimisation and Simplification
The consequent rules of an induction graph may be optimised and
simplified. The applicable methods are:
- detection and elimination of recurring premises;
- use of a symbolic algorithm exploring the whole description domain;
- algorithm of Quinlan [1987]: a hill-climbing for search the minimum
pessimistic error rate.
f - Technical Limitations
The capacities of the software are:
- 16.384 attributes
- 2^32 - 1 cases,
Actually, the limitations are those of the computer.
g - Acknowledgements
We would like to thank all the people (more than 1000 from 30 countries) who
did contribute, by their remarks, their suggestions and their encouragement,
to an adequate evolution of the software SIPINA_W.
Previous6NextTop
>~~~Positions:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Subject: GMD Postgraduate Program - Candidates sought in ML/Data Mining
Date: Thu, 05 Dec 96 14:17:13 +0100
From: Stefan.Wrobel@gmd.de
GMD Postgraduate Program - Candidates sought in ML/Data Mining
GMD's postgraduate program offers a two-year position
(renewable for a third year) to young scientists who have just
completed (or are about to complete) their Ph.D.
In particular, we are looking for candidates with a background
in Machine Learning/Data Mining/Knowledge Discovery in Databases
to work in GMD's FIT institute on ML/Data Mining topics. You
would be working in an active group and receive a very competitive
salary. For more information on our group and institute, see http://nathan.gmd.de/projects/ml/home.html.
Deadline for application is Dec. 15, 1996, so interested applicants
should get in touch with us as soon as possible. Contact:
Stefan Wrobel, E-Mail stefan.wrobel@gmd.de
A final reminder that the AI and Statistics Workhop will be
held from January 5th to January 7th at the Bahia Mar hotel
in Fort Lauderdale, Florida. The workshop program will be preceded
on January 4th by what promises to be a day of very interesting
tutorials on such topics as
- Conditional independence in statistics and AI (A. P. Dawid),
- Bayesian time series analysis and forecasting (Mike West),
- Learning in information agents (Tom Mitchell), and
- Graphical models, neural networks, and machine learning algorithms
(Mike Jordan).
If you are planning on attending please note that the workshop
hotel will relinquish the block of rooms reserved for workshop
attendees on December 20th: since the hotel is fully booked it
is essential you make reservations before the 20th.
Full details (including registration forms and workshop program)
are available at:
Previous8NextTop
>~~~Meetings:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Tue, 10 Dec 1996 16:57:19 +0000
From: Steve Cartmell (steve@pap.com)
Subject: PADD97
PADD97 - The First International Conference and Exhibition on
=============================================================
The Practical Application of Knowledge Discovery and Data Mining
==========================================================================
IN RESPONSE TO NUMEROUS REQUESTS PLEASE NOTE THAT THE PAPER SUBMISSION
DEADLINE HAS BEEN EXTENDED TO JANUARY 24, 1997
Wednesday 23rd April - Friday 25th April 1997, London, UK
*UPDATE*
TUTORIALS
Usama Fayyad, Microsoft Research
Evangelos Simoudis, IBM
DATA Mining and the KDD Process
Ken Totton, Huw Roberts, BT Laboratories
Knowledge Discovery - Practical Methodology and Case Studies
Luc De Raedt, Catholic University of Leuven
Principles and Practice of Inductive Logic Programming
INVITED SPEAKERS
Stephen Muggleton, Oxford University
Usama Fayyad, Microsoft Research
Call for Papers
We invite you to submit a paper, industrial report, case study or white
paper, describing fielded applications which exploit KD and DM technology
and which emphasize the following aspects:
* Actual business benefits and business problems addressed
* Either innovative KD and DM techniques applied to standard domains
or significant new applications of standard techniques
* Issues and methods of resolution to get the application
implemented and deployed
* why KDD was appropriate
* How benefits are measured
Papers can be of any length, up to a maximum of twenty pages, and on
virtually any KD and DM related topic, which might include, but are not
limited to the following:
Rule Induction
Classification
Clustering
Summarisation
Learning Systems
Inductive Logic Programming
Pattern Recognition
Predictive Modelling
Neural Networks
Client/Server Systems
Intelligent Agents
Knowledge Acquisition
Decisional Data Analysis
Visualisation
Dependency Detection
Uncertainty Handling
Sequence Processing
Regression Methods for Prediction
Database issues in Data Mining
Since PADD97 focuses on the real-world benefits of KD and DM technology,
preference will be given to papers describing fielded applications,
especially in the business arena. Also of interest are descriptions of
advanced prototypes, tools and techniques, and general survey papers which
indicate the future direction this important technology will take. Authors
must clearly state whether the systems described are in routine use, and
discuss the criteria used for assessing the performance of both fielded
systems and advanced prototypes.
Dates:
Submission Deadline: January 24th , 1997
Notification: February 21st, 1997
Final Papers due: March 21st, 1997
Submission Details
Five copies of papers written in English, should be received by the
conference organiser, at the address below, on or before January 24th,
1997.
Please include a short abstract, and covering page containing full contact
details and e-mail address. Authors of successful submissions will be
contacted by February 21st, 1997. Accepted papers (in a camera-ready
format) will be due by March 21st, 1997.
Speakers are expected to present their papers in person, and participate in
the conference at the special reduced speaker rate.
Accepted papers will be published in the PADD97 proceedings.
If submitting your paper by courier please use the following address.
PADD97
54 Knowle Avenue
Blackpool
Lancs FY2 9UD
UK
PA EXPO97
PADD will form part of a five day Practical Application Expo which will
also include PAP97-The Practical Application of Prolog, PACT97-The
Practical Application of Constraint Technology and PAAM97-The Practical
Application of Intelligent Agent and Multi Agent Technology.
For further information on PADD and PA EXPO97 please visit our Web site:
The Conference is organised by The Practical Application Company. The
Sponsorship and Exhibition Coordinator is Clive Spenser, PMG Treasurer, and
Marketing Director of Logic Programming Associates.
-----------------------------------------------------------------------------
To register interest please take the time to fill in this quick reply form
and send it to mining@pap.com
Name:
Position:
Organisation:
Address:
Postcode:
Country:
Telephone:
Fax:
E-mail:
URL:
[ ] I may submit a paper to PADD97 (Give provisional title if possible)
[ ] I may wish to attend PADD97 as a delegate
[ ] My company may wish to sponsor or exhibit at the event
[ ] I have no interest in this technology. But keep me informed of other
events in your advanced software technology series
[ ] I am interested in other events at the Practical Application Expo
[ ] I have no interest in this. Please remove me from the mailing list
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/
Previous9NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Thu, 5 Dec 96 10:45:59 EST
From: Tom Fawcett (fawcett@nynexst.com)
To: kdd@gte.com
Subject: CFP: AAAI-97 Workshop on AI in Fraud Detection and Risk Management
I'm posting this announcement to the ML and KDD lists because we're very
interested in adaptive approaches to these problems.
-Tom
--------------------
CALL FOR PARTICIPATION
The AAAI-97 Workshop on
AI Approaches to Fraud Detection and Risk Management
DESCRIPTION:
Fraud detection and risk management involve monitoring the behavior of
populations of users in order to estimate, detect or avoid undesirable
behavior. Undesirable behavior is a broad term including delinquency,
fraud, intrusion and account defaulting. This workshop will bring together
researchers in these areas to discuss approaches and experiences in dealing
with the critical issues:
- large volumes of data
- highly skewed distributions
- changing distributions
- widely varying error costs, and costs changing over time
- adaptation of undesirable behavior to detection techniques
- changing patterns of legitimate behavior
- trading accuracy for timely decisions
- social issues (privacy, discrimination, 'redlining')
TOPICS:
Papers on the following, and related, areas and approaches are invited:
Credit/calling card fraud Computer/network intrusion
Internet transaction fraud Insurance fraud
Cellular fraud Insider trading
Credit rating/approval Prediction of delinquency/bad debt
Machine learning Neural networks
Probabilistic modeling Decision Theory
Genetic algorithms Knowledge discovery and data mining
Knowledge-based systems Statistical approaches
FORMAT:
This will be a one-day workshop comprising mainly technical presentations.
An overview, panel discussion, and/or invited talk will be selected to
reflect the topics of interest to the participants.
ATTENDANCE:
20-40 people will be invited based on experience as indicated by paper
submissions or research summaries.
SUBMISSION REQUIREMENTS:
Authors should submit papers of no more than 8 pages to the workshop
chair at the address below. We encourage not only papers on completed
and ongoing projects, but also thorough discussions of domains and
problematic issues. Submissions should include authors' e-mail and
surface mail addresses, a short abstract, and a list of keywords.
E-mailed submissions of Postscript files are preferable; if a
Postscript file cannot be produced, submit four (4) printed copies.
We encourage each author to describe the difficult issues manifest in the
domain, how the system deals with these issues, and how the system's
performance can be evaluated. Papers discussing significant unsolved
problems (e.g., dealing with social considerations) will also be considered.
People interested in attending but not presenting a paper should submit a
one-page summary of relevant interests and work.
SUBMISSION DEADLINE: March 11, 1997
NOTIFICATION DATE: April 1, 1997
FINAL DATE FOR CAMERA-READY COPIES: April 22, 1997
SUBMIT TO WORKSHOP CHAIR:
Tom Fawcett
NYNEX Science and Technology
400 Westchester Avenue
White Plains, NY 10604
E-mail: fawcett@nynexst.com
Phone: (914) 644-2193
FAX: (914) 949-9566
ORGANIZING COMMITTEE:
Tom Fawcett
NYNEX Science and Technology
fawcett@nynexst.com
Ira Haimowitz
GE Corporate Research and Development
haimowitz@crd.ge.com
Foster Provost
NYNEX Science and Technology
foster@nynexst.com
Sal Stolfo
Columbia University
stolfo@cs.columbia.edu
Previous10NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Blaz Zupan (Blaz.Zupan@ijs.si)
Subject: CFP: Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-97)
Date: Sun, 8 Dec 1996 15:03:24 +0100 (MET)
IDAMAP-97
INTELLIGENT DATA ANALYSIS IN MEDICINE AND PHARMACOLOGY
First Call for Papers for the Workshop at
IJCAI-97
15th International Joint Conference on Artificial Intelligence
August 23-29, 1997
Nagoya, Japan
Organized by:
Nada Lavrac, J. Stefan Institute, Slovenia (chair)
Pedro Barahona, Universidade Nova de Lisboa, Portugal
Riccardo Bellazzi, University of Pavia, Italy
Paul Cohen, University of Massachusetts, USA
Werner Horn, Austrian Research Institute for Artificial Intelligence
Elpida Keravnou, University of Cyprus (co-chair)
Cristiana Larizza, University of Pavia, Italy
Hiroshi Tanaka, Tokyo Medical and Dental University, Japan
Blaz Zupan, J. Stefan Institute, Slovenia (co-chair)
GENERAL INFORMATION
IDAMAP-97, a one day IJCAI-97 workshop, will be held in Nagoya, Japan,
in the period of August 23-25, immediately prior to the start of the
main IJCAI conference. The exact date will be determined soon.
Gathering in an informal setting, workshop participants will have the
opportunity to meet and discuss selected technical topics in an
atmosphere which fosters the active exchange of ideas among
researchers and practitioners. To encourage interaction and a broad
exchange of ideas, the workshop will be kept small, preferably under
30 participants and certainly under 40. Attendance will be limited to
active participants only. The workshop is intended to be a genuinely
interactive event and not a mini-conference, thus ample time will be
allotted for general discussion. The workshop will last one full
day. Attendees at the workshop will have to register for the main
IJCAI conference.
TOPIC
The gap between data generation and data comprehension is widening in
all fields of human activity. In medicine and pharmacology overcoming
of this gap is particularly crucial since medical decision making
needs to be supported by arguments, based on basic medical and
pharmacological knowledge as well as knowledge, regularities and
trends extracted from data by intelligent data analysis techniques.
Computational methods for intelligent data analysis are aimed at
narrowing the gap between data gathering and data comprehension. For
example, effective machine learning tools exist that can be used to
generate understandable diagnostic and prognostic rules. One of the
main topics of the workshop are techniques for intelligent data mining
and knowledge discovery, focused on applications of intelligent data
analysis in medical and pharmacological databases. Further main
topics of the workshop are applications of clustering, data
visualization, interpretation of time-ordered data (derivation and
revision of temporal trends and other forms of temporal data
abstraction), learning with case bases, discovery of new diseases, new
drug compounds, predicting drug activity, etc. Special emphasis will
be given to solving problems which result from the automated data
collection in modern hospitals, such as analysis of computer-based
patient records (CPR), analysis of data from patient-data management
system (PDMS), intelligent alarming, as well as effective and
efficient monitoring.
SCIENTIFIC PROGRAM
The scientific program of the workshop will consist of presentations
of accepted papers and panel discussions.
Papers are invited both on methodological issues of data mining as
well as on specific applications in medicine and pharmacology. The
preferred length of papers is 10 pages.
Panel discussions will consist of commentators' views on the presented
papers as well as on discussions initialized by participants. In order
to be able to organize these discussions, entries for discussions are
encouraged on any topic related to the workshop. We especially
encourage entries on the topic 'Data mining and knowledge discovery -
its practical potential in medicine and pharmacology'. The preferred
length of entries for panel discussions is 1 page.
SUBMISSION OF PAPERS
Submit 8-12 page papers by e-mail (postscript) and 3 hard-copies by
surface mail to:
Nada Lavrac, Blaz Zupan
J. Stefan Institute
Jamova 39
SI-1000 Ljubljana
Slovenia
tel. +386 61 177 3272, 177 3380
fax. +386 61 125 1038, 219 385
email: idamap97@ijs.si
Submissions should be received no later than March 3, 1997, and must
include first author's complete contact information, including
address, email, phone, and fax number.
WORKSHOP PARTICIPATION
Workshop participation is not limited only to authors of
submissions. A limited number of other attendees will be selected
based on submitted statements of interest for participation at the
workshop. A statement of interest, preferably with an entry for panel
discussion (send an email to idamap97@ijs.si),
should include the
name, address, email, phone, fax, and description of research
interests.
IMPORTANT DATES
- Paper submission deadline March 3, 1997
- Notification to authors March 26, 1997
- Camera-ready papers April 15, 1997
PUBLICATION OF PAPERS
Accepted papers will be published in the IDAMAP-97 working notes. It
is planned to publish a post-conference publication based on selected
workshop papers.
WORKSHOP FEE
Workshop fee is US$ 50 per participant, in addition to the normal
IJCAI-97 registration fee.
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