*** Only 24 till KDD-96 paper submission deadline ***
--
Data Mining and Knowledge Discovery community,
focusing on the latest research and applications.
Contributions are most welcome and should be emailed,
with a DESCRIPTIVE subject line (and a URL, when available) to (kdd@gte.com).
E-mail add/delete requests to (kdd-request@gte.com).
Nuggets frequency is approximately weekly.
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, URL http://info.gte.com/~kdd.
-- Gregory Piatetsky-Shapiro (moderator)
********************* 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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Incrementalism is the enemy of innovation
Nicholas Negroponte, MIT Media Lab
Previous1NextTop
>~~~Publications:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: xindong@insect.sd.monash.edu.au
Date: Sun, 18 Feb 1996 11:13:58 +1100 (EST)
Subject: KDD Book Announcement
Author: Xindong Wu
Book title: Knowledge Acquisition from Databases
Publisher: Ablex, USA
Year: 1995
ISBN: 1-56750-206-7 (cloth cover);
1-56750-205-9 (paper cover)
Knowledge acquisition from databases is a research frontier for both
database technology and machine learning techniques, and has seen
sustained research in recent years. It also acts as a link between
the two fields, thus offering a dual benefit. First, because database
technology has already found wide application in many fields, machine
learning research obviously stands to gain from this greater exposure
and established technological foundation. Second, machine learning
techniques can augment the ability of existing database systems to
represent, acquire, and process a collection of expertise such as
those that form part of the semantics of many advanced applications,
for example, computer-aided design (CAD) and computer-aided
manufacturing (CAM).
This book contains three parts. Part I surveys the area of knowledge
acquisition from databases and figures out some of the major problems.
Part II provides an overview of symbolic methods in machine learning
and describes two types of rule induction algorithms to facilitate the
acquisition of knowledge from databases: the decision tree-based
ID3-like algorithms and the extension matrix-based induction
algorithms. The author's own HCV induction algorithm based on the
newly developed extension matrix approach is described as a
counterpart to ID3-like algorithms. Two practical issues, noise
handling and processing real-valued attributes in the context of
knowledge acquisition from databases, are addressed in detail, and a
performance comparison of different learning algorithms (ID3, C4.5,
NewID, and HCV) is also provided in terms of rule compactness and
accuracy on a battery of experimental data sets including three famous
classification problems, the MONK's problems. Finally, in Part III,
an intelligent learning database system, KEshell2, which makes use of
the HCV algorithm and couples machine learning techniques with
database and knowledge base technology, is described with examples.
The parts of the book have different but interrelated objectives and
suit different levels of readership. Part II can be adopted as an
inductive learning module in an artificial intelligence (AI) related
undergraduate and/or postgraduate course. Part III can be integrated
into a machine learning or advanced database course. Together with the
brief overview in Part I, this book as a whole should be of interest
to the whole intelligent databases and machine learning community and
to students in machine learning, expert systems, and advanced database
courses. Knowledge acquisition from databases could well form an
independent honors or postgraduate course in a computer science or
information systems program, and therefore this book could be adopted
as a textbook.
The book is based on the author's papers and reports produced over the
past few years. Contact details for the publisher and a short
PostScript file with a table of contents can be found at the following
web address: http://www.sd.monash.edu.au/~xindong/Publication/KDD.html.
Previous2NextTop
>~~~Siftware:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: David Hovel (davidhov@microsoft.com)
Subject: MS Research Decision Theory Group Announcement
Date: Thu, 15 Feb 1996 09:12:46 -0800
ANNOUNCING MSBN, THE MICROSOFT BAYESIAN NETWORKS MODELING TOOL
The members of the Decision Theory group at Microsoft Research have
developed a Bayesian network construction and evaluation tool called
Microsoft Bayesian Networks (MSBN). This program and its component parts
are being made available free of charge for non-commercial uses by academic
organizations and research institutions.
MSBN is a 32-bit Bayesian network modeling tool which runs on the Windows95
and Windows NT platforms. It supports the creation and manipulation of
Bayesian networks as well as their evaluation. The product and its
components are provided on an as-is basis.
MSBN is primarily composed of two executable files. The user interface
program is MSBN32.EXE, which is written in Visual Basic 4.0. Inference and
data management support are provided by MSBN32.DLL which is written in C++.
The interface between the two binaries is fully documented and accessible
from either Visual Basic or C++. A complete set of function declarations
for Visual Basic is provided. In other words, the DLL can be used to
construct alternate interfaces using Visual Basic, C++ or other Windows
development languages.
HOW DO I GET MSBN?
Connect to the URL given above and print the MSBN Usage Agreement. Sign
and mail the agreement along with your email information to Microsoft
Corporation. After the agreement is filed at corporate headquarters, you
will receive, via email, FTP logon information which will allow you to
download the compressed library.
FYI, Knowledge Industries also provides a no-cost license for academic,
non-commercial use of its Bayesian-network modeling tool. KI announced the
license to the UAI mailing list a few weeks ago. With apologies for
intersections in the distribution lists, we forward a copy of the original
posting:
<---- Begin Forwarded Message ---->
From: shwe@kic.com
Subject: Knowledge Industries announces no-cost academic licenses for
Bayesian-network software
To: uai@maillist.cs.orst.edu
Knowledge Industries (KI) of Palo Alto, CA is pleased to announce to the
academic and research communities that KI is providing no-cost licenses for its
DX Solution Series software. This software allows you to build expert systems
based on Bayesian networks. The licenses allow users to freely use KI modeling
and inference software for academic research and teaching. Academic sites
already taking advantage of the availability of KI software for research and
teaching include departments at the University of Washington and Stanford
University.
The DX Solution Series consists of three principal components: a graphical
knowledge acquisition tool called DXpress, a sample runtime interface called
WIN-DX, and a set of embeddable inference libraries called API-DX. Currently,
the software supports Windows 3.1, Windows NT 3.5x, and Windows 95. DXpress is
a robust knowledge acquistition tool for building Bayesian networks. DXpress
uses several knowledge acquisition acceleration techniques to reduce the amount
of time needed to develop an expert system, including causal independence and
probability partitions. DXpress also provides forms for entering auxiliary
information used in most runtime appliations, such as questions and definitions
for observations. Written entirely in C++, DXpress rapidly updates its
graphical windows even when large networks are loaded.
To provide a rapid develop-and-test environment, DXpress automatically calls the
WIN-DX runtime system, in which you can instantly see the effects of changes to
your knowledge base. Alternatively, you can build your own runtime system using
API-DX, accessing API-DX from Visual C++ or Visual Basic. The KI software will
be kept up-to-date on standards adopted by the UAI community for interchange of
Bayesian-network models.
For more information on KI and the DX Solution Series, please refer to the KI
web site: http://www.kic.com.
You will also find on the KI web site a copy of
the KI academic license agreement, which you may download, print, sign, and
return to KI for a no-cost license to use the KI software. In addition, you may
send email to ki@kic.com,
call us at 415-321-0400, or fax us at 415-322-3554.
Department of Statistics,
University of Washington
Tenure Track Assistant Professor position, beginning September 1996
(pending approval). Requires Ph.D in Statistics or in a related field.
Duties include teaching undergraduate and graduate courses, and research.
The Department is seeking to strengthen its cross-disciplinary ties and
hopes to attract an applicant with research interests in this direction.
Temporary Assistant Professor position, beginning September 1996 (pending
availability of funds). Requires Ph.D in Statistics or in a related field.
Appointment will be for one year. Duties include teaching and research.
The position might also have a substantial consulting component.
Send application, resume, copies of publications, and four recommendation
letters by February 28 to: Statistics Faculty Search Committee, Department
of Statistics, Box 354322, University of Washington, Seattle, WA
98195-4322, USA.
The University of Washington is building a multicultural faculty and
strongly encourages applications from female and minority candidates.
AA/EOE.
Dear Professor Piatetsky Shapiro
There were some rather strange errors in the text of the KDD newsletter
which we have just seen - could you please replace the existing text with
the following.
DATA MINING '96
LONDON, 25-26 APRIL 1996
Sponsored by:
BCS SGES
AI Watch - the Newsletter of AI INTELLIGENCE
Background and Objectives
Many organisations have collected large amounts of data recording
their past activities. Buried within these databases is knowledge,
from which can be learnt important lessons which, in turn, can be
exploited to improve future performance. The extractio n of this
knowledge, often in the form of a number of rules which describes how
one or more fields are related to other fields, is known as data
mining or KDD (Knowledge Discovery in Databases).
The techniques used in KDD exploit some of the most recent research in
artificial intelligence and machine learning. A fundamental purpose
of this Seminar is to gather together both academics and
representatives from industry in order to review the curr ent
techniques and to discuss their practical application.
Industry and commerce have begun to see the potential of these
techniques and have started to exploit them in a wide range of
applications such as market segmentation, risk analysis, credit rating
and customer profiling. Data mining techniques have also been used by
social service departments and there is huge potential for medical
data mining. Case studies for a wide range of applications will be
presented.
Anyone wishing to apply these tools needs to be aware of the
availability and use of data mining toolkits. There are a number on
the market and some software is available on the World Wide Web. These
will be assessed both in case studies and in comparativ e studies.
Programme
Day one
Keynote presentation
Inductive Query Languages
Arno Siebes, Centre for Mathematics and Computer Science, The
Netherlands
State of the art in the data mining area
Inductive Query Languages: data mining technology for users rather than data
mining experts
'Interest Subsets' as an Inductive Query Language
Thegenerality of interesting subsets
The KESO project: building a second generation data mining system
State of the art in Data Mining and Knowledge Discovery in Databases
Willi Kloesgen, GMD, Sankt Augustin, Germany
Definition of KDD and data mining * Why is KDD necessary When can KDD be
applied * The KDD process and its tasks Methods for KDD tasks KDD
- tools and systems
- applications
- architectures
Data Mining Using Modern Heuristic Techniques
V J Rayward Smith, University of East Anglia
Viewing data mining as a optimisation problem
Applying heuristic optimisation techniques to data mining:
traditional local search
genetic algorithms
simulated annealing
tabu search
hybrid systems
Case studies
Choosing the Right Data Mining Solution
Sarabjot S Anand et al, University of Ulster
Navigating the hype, buzz words and acronyms
Asking the right questions:
- Can I get what I want from the data I have?
- Is my data in a state that allows me to mine it?
- How will my running a Data Mining system on my data affect my
existing OLTP operations?
Distributed Database Management for Uncertainty Handling in Data
Mining Sally McClean and Bryan Scotney, University of Ulster
Combining data from different databases to produce new knowledge
integrating the aggregates
achieving lower levels of granularity
identifying new knowledge which could not have been found previously
Automatic Induction of Rules from Examples: a Critical Review
Max Bramer, University of Portsmouth
The knowledge elicitation bottleneck
The ID3 algorithm and its derivatives
Rule induction in practice
Strengths and weaknesses of the ID3 approach
Inducing decision trees v. modular rules - The Prism Project
Using knowledge to guide rule induction - The Cupid Project
Using induction to capture non-verbal skills
Directions for further research
The Specification and Implementation of Data-Defined Problems
Derek Partridge, University of Exeter
Data mining as the domain of data-defined problems
Software engineering and data-defined problems
Inductive programming techniques
A multiversion methodology for reliable data mining
Some examples
Conclusion
Knowledge Discovery in Large Databases on Data Compression and
Conceptual Clustering Salem al-Naemi and Jorge Bocca, University of
Birmingham
Using the process of conceptual clustering in KDD
Defining
a clustering algorithm measure based on entropy
AOCCA - An Attributed Oriented Conceptual Clustering Algorithm
A KDD methodology to extract various qualitative and/or quantitative
knowledge rules Implementationand analysis
Conclusion
Day Two
Keynote Presentation
> From Data Mining to Knowledge Discovery: the Roadmap
Gregory Piatetsky-Shapiro, GTE Laboratories, USA
The rapidly growing databases are overwhelming the traditional, ad-hoc
methods of data analysis, while hiding many potentially valuable
nuggets of knowledge. This creates a need for a new, automated
approach for making sense of the data - the domain of a n emerging
field called Data Mining and Knowledge Discovery in Databases (KDD).
KDD combines techniques of machine learning, expert systems,
databases, statistics and data visualisation to create a new
generation of intelligent and automated tools for di scovery in data,
which are already being applied in many areas of business, science and
government all around the world. This presentation provides an
overview of KDD, focusing on Data Mining goals and methods; Survey of
available data mining tools and Internet Resources; The steps of the
knowledge discovery process; Application development challenges and
pitfalls; Examples of successful data mining applications
Data Mining in BT
Ken Totton and Huw Roberts, Data Mining Group, BT Laboratories
Overview of BT's approach to data mining
The data mining process
Case Studies
Data Mining for Data Owners
Colin Shearer, Integral Solutions Ltd
Data owners: who they are and why they are the key to successful data
mining projects
Barriers to data owner involvement: technology,
complexity and accessibility issues
Presenting data mining technology
to data owners Case studies: successful examples of data mining by
data owners
Experiences of Data Mining in a Financial Services Company
J C W DeBuse and B de la Iglesia, University of East Anglia
Using data mining tools for commercial data: the key features
Survey of currently available tools and algorithms
Experiences of applying these tools tocommercial databases Performance
evaluation
Improving Customer Retention (through Knowledge Guided Data Mining)
Rob Milne, Intelligent Applications Ltd
Much more profit can be made from existing customers than new customers This
increase in profitability can be very high
Knowledge Guided Data Mining techniques
are very effective at predicting those customers most likely to change
A case study will be presented in which over 90% accuracy of
predictions was achieved - this would have been impossible with
traditional approaches. Guiding the data mining with knowledge
provided the critical success factor.
Data Mining and Data Visualisation with Silicon Graphics Technology
Chris Hardy, Silicon Graphics
Data mining tools for visualising and
analyzing data with demonstrations
Case Studies
Business Applications of Statistics for Data Mining - Getting the
Basics Right
Jon Petersen, SPSS UK Ltd
An overview of statistical
tools for data mining
The advantages of statistical techniques
Predictive tools and Classification tools
The use of one statistical technique, CHAID, which is especially suitable to
data mining problems
The use of statistical techniques to complement other techniques such
as Neural Networks
Examples of the use of statistics to gain
competitive advantage, illustrated by case studies
End of Conference
This event is part of a series of Seminars and Tutorials, to be held
in London from 22-26 April 1996, at Chelsea Village, Fulham Road.
Other topics include Intelligent Systems for Finance and Commerce
25-26 April Intelligent Data Management 24-25 April Uncertainty in
Information Systems 24 April Building the Data Warehouse 22-23 April
Developments in Database Technology 24 April Data Warehousing and
Parallel DB Servers 24-25 April Enterprise Client/Server 24-25 April
Rapid Application Delivery for Client/Server 26 April Middleware 26
April OLAP Tutorial and EIS & OLAP Seminar 23-25 April
This series is complemented by an exhibition of related products and
services
Price: One day =GBP395; 2 days 695; 3 days 950; 4 days 1275; 5 days
1550. V.A.T. at 17.5% is charged on all fees.
SUBSTANTIAL ACADEMIC DISCOUNTS AVAILABLE. APPLY TO UNICOM FOR DETAILS
For futher information on attending, exhibiting products and services,
contributing a paper, purchasing the proceedings or details of
UNICOM's Data Mining or Data Warehousing Reports Please contact
UNICOM @UNICOM.CO.UK.
telephone +44 1895 256 484 fax +44 895 813 095.
-----------------------------------------------
Professor Sally McClean,
Division of Mathematics,
School of Information and Software Engineering,
University of Ulster,
Coleraine,
Northern Ireland BT52 1SA.
Telephone 44-1265-324602
www: http://www.infc.ulst.ac.uk/informatics/personnel/si.mcclean.html
Fax number 44-1265-324916
e-mail SI.McClean@ulst.ac.uk
----------------------------------------------
Previous6NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Wed, 14 Feb 1996 03:05:51 -0400 (AST)
From: Lev Goldfarb (goldfarb@unb.ca)
Subject: Workshop: What is inductive learning?
Call for extended abstracts:
WHAT IS INDUCTIVE LEARNING?
On the foundations of AI and Cognitive Science
Toronto - Canada
May 20 - 21, 1996
A workshop in conjunction with the 11th Biennial
Canadian AI Conference to be held at the Holiday
Inn on King, Toronto during 21 - 24 May 1996
This workshop is a long overdue attempt to look at the inductive learning
process (ILP) as the central process generating various representations of
objects (events).
To this end one needs, first of all, to have a working definition of the
ILP, which has been lacking. Here is a starting point: ILP is the process
that constructs class representation on the basis of a (small) finite set
of examples, i.e. it constructs the INDUCTIVE class representation. This
class representation must, in essence, provide INDUCTIVE definition (or
construction) of the class.
The constructed class representation, in turn, modifies the earlier
representation of the objects (within the context specified by the ILP).
Thus, any subsequent processes, e.g. pattern recognition, recall, problem
solving, are performed on the basis of the newly constructed object
(event) representations. To put it somewhat strongly, there are only
inductive representations.
Two main and strongly related reasons why ILPs have not been perceived as
the very central processes are a lack of their adequate understanding and
a lack of their satisfactory formal model. Most of the currently popular
formal models of ILPs (including connectionist models) do not provide
satisfactory inductive class representations. One can show that inductive
class representations (in other words, representations of concepts and
categories) cannot be adequately specified within the classical (numeric)
mathematical models.
We encourage all researchers (including graduate students) seriously
interested in the foundations of the above areas to participate in the
workshop. Both theoretical and applied contributions are welcomed
(including, of course, those related to vision, speech, and language).
While extended abstracts will be available at the workshop, we are
planning to publish the expanded and reviewed versions of the
presentations as a special issue of journal Pattern Recognition.
Submit a copy (or e-mail version) of a 3-4 page extended abstract to
Lev Goldfarb
ILP Workshop Chair
Faculty of Computer Science
University of New Brunswick
P.O. Box 4400 E-mail: goldfarb@unb.ca
Fredericton, N.B. E3B 5A3 Tel: 506-453-4566
Canada Fax: 506-453-3566
E-mail submissions are encouraged.
Important dates:
----------------
Extended abstract due: Monday, March 25, 1996.
Notification & review back to the author: Friday April 5, 1996.
Final extended abstract due: Monday April 22, 1996.
For more information about the Canadian AI Conference which is held in
conjunction with two other conferences (Vision Interface and Graphics
Interface) see:
Please e-mail to me:
_____________________________________________________________________________
I intend to submit an abstract __
I plan to attend the workshop __
_____________________________________________________________________________
Previous7NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Thu, 15 Feb 1996 12:11:59 -0400
From: Ravi Krovi (krovir@athena.ncat.edu)
Subject: AI minitrack
I am enclosing a CFP for an AI minitrack. Some of the focus is on
using machine learning techniques for problems such as classification.
Please contact me if you are interested.
Ravi
(krovir@athena.ncat.edu)
===========================================================================
ASSOCIATION FOR INFORMATION SYSTEMS
August 16 - 18, 1996.
Phoenix, Arizona.
***************
CALL FOR PAPERS
***************
MINITRACK
ARTIFICIAL INTELLIGENCE APPLICATIONS IN BUSINESS DECISION PROCESSES
===========================================================================
DESCRIPTION:
============
This minitrack is a forum for the interaction and sharing of ideas
among researchers interested in AI applications in the business domain.
Submissions may draw their content from topics that examine, but are not
limited to, any combination of the following categories:
Application Areas
-----------------
Classification, Forecasting, Decision Processes, User Interfaces,
Education, Business Process Re-engineering
Techniques
----------
Case Based Reasoning, Genetic Algorithms, Classifier Systems,
Neural Systems, Expert Systems, Hybridized Systems
Theoretical Frameworks
----------------------
Garbage cans, Physical Symbol Hypothesis, Artificial Adaptive Agents,
DAI, Fuzzy Logic (Possibility theorems), Cognitive Models of End Users
based on representation, learning, and adaptation (SOAR, ACT-STAR, Frame
theory, Semantic networks etc.), Spatial and Temporal Reasoning, Uncertainty
models (Prospect theory), Chaos theories
Methodological Refinements
--------------------------
Experimental Evaluations, Analysis of domains in which AI based models are
appropriate, Architectural descriptions, Simulations, Theoretical
Foundations, Philosophical foundations (role of AI in DSS research),
Developmental and usage experiences with AI based systems (could include
technical, economic, social, and behavioral impact etc.)
SUBMISSION GUIDELINES
=====================
All conference submissions and the review processes will be managed
through E-mail. Details are as follows:
Format -- ASCII
Length -- Maximum of 3 pages (up to 1750 words)
(please mention if a full paper is available)
Dates
Deadline -- March 1, 1996.
Notification of Acceptance -- April 15, 1996.
Camera Ready Copy due -- May 24, 1996.
Contact Person -- Ravi Krovi
(krovir@athena.ncat.edu)
=========================================================================
MINITRACK CHAIRS:
-----------------
Ravi Krovi, North Carolina A&T State University
Akhilesh Chandra, North Carolina A&T State University
Balaji Rajagopalan, Southern Arkansas University.
=======================================================================
======================================================================
Ravi Krovi, Ph.D. Phone: 910-334-7656 (4034)
Merrick Hall Fax: 910-334-7093
North Carolina A&T State University E-Mail: krovir@athena.ncat.edu
Greensboro, NC 27411.
======================================================================
KDD Nuggets Index