*
M. Bramer, IEE Colloquium on Knowledge Discovery,
London, October 17-18 1996
--
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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Manuscript: something submitted in haste and returned at leisure.'
- Oliver Herford (1863-1935)
Visit the KDD-96 Web page at http://www-aig.jpl.nasa.gov/kdd96
to get details on how to register for the KDD-96 Conference to
be held August 2-4, Portland, OR.
Also recently added to the Web page are the invited speakers
and abstracts for KDD-96 including Georges Grinstein (U. Mass
at Lowell and MITRE), Jeffrey D. Ullman (Stanford University),
Vladamir Vapnik (AT&T Research Laboratories) and Perry Youngs
(Sara Lee Corporation).
(The KDD-96 Conference is being held the weekend before AAAI-96
with which it is collocated and is being held at roughly the same
time as UAI-96: thus, conference junkies can attend 3 conferences over
a few days). Previous2NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Jiawei Han (han@cs.sfu.ca)
Date: Mon, 29 Apr 1996 21:01:00 -0700 (PDT)
Subject: A list of accepted papers (with full authors and associations)
(see also http://www-aig.jpl.nasa.gov/kdd96
---------------------------------
KDD'96 Conf. Presentation Papers:
---------------------------------
K008 AL
Jason T. L. Wang (New Jersey Inst. Tech.), Bruce A. Shapiro (US National
Inst. Health), Dennis Shasha (New York Univ.), Kaizhong Zhang (Univ.
Western Ontario, Canada ), Chia-Yo Chang (New Jersey Inst. Tech.),
'Automated Discovery of Active Motifs in Multiple RNA Secondary Structures'
K022 AL
Andrzej Czyzewski (Tech. Univ. Gdansk, Poland),
'Mining Knowledge in Noisy Audio Data'.
K026 AL
Andreas Ittner (Chemnitz Univ. Technology, Germany) and Michael Schlosser
(Fachhochschule Koblenz, Germany),
'Discovery of Relevant New Features By Generating Non-Linear Decision Trees'.
K033 AL
Pedro Domingos (UC-Irvine),
'Efficient Specific-to-General Rule Induction'.
K041 AL
Tom Fawcett and Foster Provost (NYNEX Science and Technology),
'Combining Data Mining and Machine Learning for Effective User Profiling'.
K059 AL
Martin Ester, Hans-Peter Kriegel, Joerg Sander, and Xiaowei Xu
(Univ. Munich, Germany),
'A Density-Based Algorithm for Discovering Clusters in Large Spatial
Databases with Noise'.
K060 AL
A.J. Feelders (Univ. Twente, Netherland),
'Learning from Biased Data Using Mixture Models'.
K061 AL
Robert Engels (Univ. Karlsruhe, Germany),
'Planning Tasks for Knowledge Discovery in Databases; Performing
Task-Oriented User-Guidance'.
K069 AL
Vic Ciesielski and Greg Palstra (Royal Melbourne Inst. Technology, Australia),
'Using a Hybrid Neural/Expert System for Data Base Mining in Market
Survey Data'.
K072 AL
Krista Lagus, Timo Honkela, Samuel Kaski, and Teuvo Kohonen
(Helsinki Univ. Tech., Finland),
'Self-Organizing Maps of Document Collections: A New Approach to Interactive
Exploration'.
K073 AL
Ruediger Wirth and Thomas P. Reinartz (Daimler-Benz Res. & Tech., Germany),
'Detecting Early Indicator Cars in an Automotive Database: A Multi-Strategy
Approach'.
K079 AL
Heikki Mannila and Hannu Toivonen (Univ. Helsinki, Finaland),
'Multiple Uses of Frequent Sets and Condensed Representations'.
K080 AL
Hannu Toivonen (Univ. Helsinki, Finaland),
'Discovering generalized episodes using minimal occurences'
K081 AL
Petri Kontkanen, Petri Myllymaki, and Henry Tirri (Univ. Helsinki, Finaland),
'Predictive Data Mining with Finite Mixtures'.
K085 AL
Stefan Wrobel, Dietrich Wettschereck, Edgar Sommer, and Werner Emde
(GMD, Germany),
'Extensibility in Data Mining Systems'.
K096 AL
Truxton Fulton, Simon Kasif, and Steven Salzberg (Johns Hopkins Univ.),
'Local Induction of Decision Trees: Towards Interactive Data Mining'.
K100 AL
Padhraic Smyth (Jet Propulsion Lab.),
'Clustering using Monte Carlo Cross-Validation'.
K108 AL
Wei-Min Shen and Bing Leng (USC),
'Metapattern Generation for Integrated Data Mining'.
K112 AL
Gregory M. Provan (Rockwell) and Moninder Singh (Univ. Pennsylvania),
'Data Mining and Model Simplicity: A Case Study in Diagnosis'.
K118 AL
Jan Zytkow and Robert Zembowicz (Wichita State Univ.),
'Automated Pattern Mining with a Scale Dimension'.
K125 AL
Shusaku Tsumoto and Hiroshi Tanaka (Tokyo Medical & Dental Univ., Japan),
'Automated Discovery of Medical Expert System Rules from Clinical Databases
based on Rough Sets'.
K138 AL
Ron Kohavi (Silicon Graphics inc.) and Mehran Sahami (Stanford Univ.),
'Error-Based and Entropy-Based Discretization of Continous Features'.
K154 AL
Kenneth A. Kaufman and Ryszard S. Michalski (George Mason Univ.),
'A Method for Reasoning with Structured and Continuous Attributes
in the INLEN-2 Knowledge Discovery System'.
K162 AL
Kamakshi Lakshminarayan, Steve Harp, Robert Goldman, and Tariq Samad
(Honeywell),
'Imputation of Missing Data Using Machine Learning Techniques'.
K164 AL
Ron Musick (Lawrence Livermore Nat. Lab.),
'Rethinking the Learning of Belief Network Probabilities'.
K166 AL
Gregory Piatetsky-Shapiro (GTE Labs), Ron Brachman (AT&T Research),
Tom Khabaza (ISL, UK), Willi Kloesgen (GMD, Germany), and
Evangelos Simoudis (IBM Almaden),
'An Overview of Issues in Developing Industrial Data Mining and Knowledge
Discovery Applications'.
K171 AL
Usama Fayyad (Microsoft Research), David Haussler (UC-Santa Cruz), and
Paul Stolorz (Jet Prop. Lab.),
'KDD for Science Data Analysis: Issues and Examples'.
K174 AL
Andreas Arning (IBM German Software Development Lab) and Rakesh Agrawal
(IBM Almaden),
'A Linear Method for Deviation Detection in Large Databases'.
K180 AL
Usama Fayyad (Microsoft Research), Gregory Piatetsky-Shapiro (GTE Labs),
and Padhraic Smyth (Jet Prop. Lab.),
'Knowledge Discovery and Data Mining: Toward a Unifying Framework'.
K182 AL
Ron Kohavi (Silicon Graphics inc.),
'Scaling Up the Accuracy of Naive-Bayes Classifers: A Decision-Tree Hybrid'.
K185 AL
Philip Chan (Florida Inst. Tech.), and Salvatore Stolfo (Columbia Univ.),
'Sharing Learned Models among Remote Database Partitions by Local
Meta-Learning'.
K189 AL
Usama Fayyad (Microsoft Research), David Haussler (UC-Santa Cruz), and
Paul Stolorz (Jet Pro. Lab.),
'A Scalable Data Mining System for Detecting Earthquakes from Space'.
K190 AL
Paul Stolorz (Jet Prop. Lab.),
'Harnessing Graphical Structure in Markov Chain Monte Carlo Learning'.
K199 AL
Rense Lange (Univ. Illinois-Springfield),
'An Empirical Test of the Weighted Effect Approach to Generalized Prediction
Using Recursive Neural Nets'
K200 AL
Masand Brij and Gregory Piatesky-Shapiro (GTE Labs),
'A Comparison of Different Approaches for Maximizing the Business Payoff
of Prediction Models'
K209 AL
B. de la Iglesia, J.C.W. Debuse, and Rayward-Smith V.J. (Univ. East Anglia),
'Discovering Knowledge in Commercial Databases Using Modern Heuristic
Techniques'.
K021 AP
David Urpani (Swinburne Univ. Tech., Australia), Xindong Wu (Monash
Univ., Australia), and Jim Sykes (Swinburne Univ. Tech., Australia),
'RITIO - Rule Induction Two In One'.
K024 AP
Einoshin Suzuki and Masamichi Shimura (Tokyo Inst. Tech., Japan),
'Exceptional Knowledge Discovery in Databases based on Information Theory'.
K043 AP
Ning Shan, Wojciech Ziarko, Howard J. Hamilton, and Nick Cercone
(Univ. Regina, Canada),
'Searching Classification Knowledge in Databases Based on Rough Sets'.
K048 AP
Stephen Mc Kearney (Univ. Bournemouth, UK), Huw Roberts (BT Labs, UK)
'Reverse Engineering Databases for Knowledge Discovery'.
K068 AP
John M. Aronis (Univ. Pittsburgh), Foster J. Provost (NYNEX Science & Tech.),
Bruce G. Buchanan (Univ. Pittsburgh),
'Exploiting Background Knowledge in Automated Discovery'.
K077 AP
Ronen Feldman (Bar-Ilan Univ., Israel), Haym Hirsh (Rutgers Univ.),
'Mining Associations in Text in the Presence of Background Knowledge'.
K086 AP
Ian W. Flockhart (Quadstone Ltd., UK), and Nicholas J. Radcliffe
(Univ. Edinburgh & Quadstone Ltd, UK),
'A Genetic Algorithm-Based Approach to Data Mining'.
K090 AP
Arno J. Knobbe and Pieter W. Adriaans (Syllogic, Netherland),
'Analysing Binary Associations'.
K091 AP
Tae-Wan Ryu and Christoph F. Eick (Univ. Houston),
'Deriving Queries from Results using Genetic Programming'.
K098 AP
Don R. Swanson and Neil R. Smalheiser (Univ. Chicago),
'Undiscovered Public Knowledge: A Ten-Year Update'.
K103 AP
Micheline Kamber (Simon Fraser Univ., Canada) and Rajjan Shinghal
(Concordia Univ., Canada),
'Evaluating the Interestingness of Characteristic Rules'.
K105 AP
Ron Rymon (Univ. Pittsburgh),
'SE-trees Outperform Decision Trees in Noisy Domains'.
K109 AP
Kevin J. Cherkauer and Jude W. Shavlik (Univ. Wisconsin-Madison),
'Growing Simpler Decision Trees to Facilitate Knowledge Discovery'.
K120 AP
Pedro Domingos (UC-Irvine),
'Efficient Specific-to-General Rule Induction'.
K131 AP
Gerald Fahner (UC-Berkeley),
'Data Mining with Sparse and Simplified Interaction Selection'.
K134 AP
David W. Cheung (Univ. Hong Kong), Vincent T. Ng (Hong Kong Polytech. Univ.)
and Benjamin W. Tam (Univ. Hong Kong),
'Maintenance of Discovered Knowledge: A Case in Multi-level Association Rules'.
K139 AP
Raymond Ng and Edwin Knorr (Univ. British Columbia),
'Extraction of Spatial Proximity Patterns by Concept Generalization'.
K142 AP
Alexander Tuzhilin and Balaji Padmanabhan (New York Univ.),
'Pattern Discovery in Temporal Databases: A Temporal Logic Approach'.
K143 AP
Takao TERANO and Yoko ISHINO (Univ. Tsukuba, Japan),
'Interactive Knowledge Discovery from Marketing Questionnaire Using
Simulated Breeding and Inductive Learning Methods'.
K150 AP
Thomas Hofmann and Joachim Buhmann (Univ. Bonn, Germany),
'Infering Hierarchical Clustering Structures by Deterministic Annealing'.
K152 AP
M. Richeldi (CSELT S.p.A., Italy) and P.L. Lanzi (Politecnico di Milano, Italy),
'Performing Effective Feature Selection by Investigating the Deep Structure
of the Data'.
K158 AP
Stefan Kramer and Bernhard Pfahringer (Austrian Research Inst. for
Artificial Intelligence, Austria),
'Efficient Search for Strong Partial Determinations'.
K173 AP
Rakesh Agrawal and Kyuseok Shim (IBM Almaden),
'Developing Tightly-Coupled Data Mining Applications on a Relational
Database System: Methodology and Experience'.
K177 AP
H.Bodek, R.L.Grossman (Magnify, Inc.), and H.V.Poor (Princeton Univ.),
'Data Mining and Tree-Based Optimization'.
K179 AP
M. Ganesh, Jaideep Srivastava (Univ. Minnesota), and Travis Richardson
(Apertus Technologies, Inc.),
'Mining Entity-Identification Rules for Database Integration'.
K184 AP
Pat Langley (Stanford Univ.),
'Induction of Condensed Determinations'.
K186 AP
George H. John (Stanford Univ.),
'Static versus Dynamic Sampling for Data Mining'.
K188 AP
Alvaro Monge and Charles Elkan (UC-San Diego),
'The field matching problem: Algorithms and applications'.
K195 AP
Yang Wang and Andrew K.C. Wong (Univ. Waterloo, Canada),
'Representing Discovered Patterns Using Attributed Hypergraph'.
International Data Corp. reports that companies that have invested in
data
warehousing, which pulls data from various large databases into smaller
ones
to analyze trends and possible business opportunities have realized a
400%
return on their investments over three-years. The study was based on 62
organizations that spent an average of $2.2 million each on their data
warehouse operations. (Investor's Business Daily 18 Apr 96 A8)
Previous4NextTop
>~~~Publications:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Tue, 16 Apr 96 17:09:49 PDT
From: Steve Minton (minton@ISI.EDU)
To: kdd@gte.com
Subject: JAIR article, Iterative Optimization and Simplification ...
Readers of this group may be interested in the following article,
which was recently published in the Journal of Artificial Intelligence
Research (JAIR):
Fisher, D. (1996)
'Iterative Optimization and Simplification of Hierarchical Clusterings',
Volume 4, pages 147-178.
Abstract: Clustering is often used for discovering structure in data.
Clustering systems differ in the objective function used to evaluate
clustering quality and the control strategy used to search the space
of clusterings. Ideally, the search strategy should consistently
construct clusterings of high quality, but be computationally
inexpensive as well. In general, we cannot have it both ways, but we
can partition the search so that a system inexpensively constructs a
`tentative' clustering for initial examination, followed by iterative
optimization, which continues to search in background for improved
clusterings. Given this motivation, we evaluate an inexpensive
strategy for creating initial clusterings, coupled with several
control strategies for iterative optimization, each of which
repeatedly modifies an initial clustering in search of a better
one. One of these methods appears novel as an iterative optimization
strategy in clustering contexts. Once a clustering has been
constructed it is judged by analysts -- often according to
task-specific criteria. Several authors have abstracted these criteria
and posited a generic performance task akin to pattern completion,
where the error rate over completed patterns is used to `externally'
judge clustering utility. Given this performance task, we adapt
resampling-based pruning strategies used by supervised learning
systems to the task of simplifying hierarchical clusterings, thus
promising to ease post-clustering analysis. Finally, we propose a
number of objective functions, based on attribute-selection measures
for decision-tree induction, that might perform well on the error rate
and simplicity dimensions.
The article is available via:
-- comp.ai.jair.papers (also see comp.ai.jair.announce)
-- Anonymous FTP from either of the two sites below.
Carnegie-Mellon University (USA):
ftp://p.gp.cs.cmu.edu/usr/jair/pub/volume4/fisher96a.ps
The University of Genoa (Italy):
ftp://ftp.mrg.dist.unige.it/pub/jair/pub/volume4/fisher96a.ps
The compressed PostScript file is named fisher96a.ps.Z (130K)
-- automated email. Send mail to jair@cs.cmu.edu or
jair@ftp.mrg.dist.unige.it
with the subject AUTORESPOND and our automailer will respond. To
get the Postscript file, use the message body GET volume4/fisher96a.ps
(Note: Your mailer might find this file too large to handle.)
Only one can file be requested in each message.
-- JAIR Gopher server: At p.gp.cs.cmu.edu, port 70.
For more information about JAIR, visit our WWW or FTP sites, or
send electronic mail to jair@cs.cmu.edu
with the subject AUTORESPOND
and the message body HELP, or contact jair-ed@ptolemy.arc.nasa.gov.
Previous5NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: fraley@stat.washington.edu
Date: Sat, 27 Apr 96 09:16:05 PDT
To: ml@ics.uci.edu,
mlnet@swi.psy.uva.nl,
bayes-news@stat.cmu.edu,
ai-stats@watstat.uwaterloo.ca,
kdd@gte.com,
uai@maillist.cs.orst.edu,
s-news@utstat.utoronto.edu
Subject: mailing list : UW Stat working group on model-based classification
The UW Stat working group on model-based classification (developers of mclust)
is starting a mailing list for occasional announcements concerning our projects
and activities.
If you would like to be included in this list, please contact me.
PURPOSE:
This is the sixth in a series of workshops which has brought together
researchers in Artificial Intelligence (AI) and in Statistics to discuss
problems of mutual interest. The exchange has broadened research in both fields
and has strongly encouraged interdisciplinary work. Papers on all aspects of
the interface between AI & Statistics are encouraged.
FORMAT:
To encourage interaction and a broad exchange of ideas, the presentations will
be limited to about 20 discussion papers in single session meetings over three
days (Jan. 5-7). Focussed poster sessions will provide the means for presenting
and discussing the remaining research papers. Papers for poster sessions will
be treated equally with papers for presentation in publications.
Attendance at the workshop will *not* be limited.
The three days of research presentations will be preceded by a day of tutorials
(Jan. 4). These are intended to expose researchers in each field to the
methodology used in the other field. The tutorial speakers are A. P. Dawid
(University College London), Michael Jordan (MIT), Tom Mitchell (Carnegie
Mellon), and Mike West (Duke University).
TOPICS OF INTEREST:
- automated data analysis and knowledge representation for
statistics
- statistical strategy
- metadata and design of statistical data bases
- multivariate graphical models, belief networks
- causality
- cluster analysis and unsupervised learning
- predictive modeling: classification and regression
- interpretability in modeling
- model uncertainty, multiple models
- probability and search
- knowledge discovery in databases
- integrated man-machine modeling methods
- statistical methods in AI approaches to
vision, robotics, pattern recognition, software agents,
planning, information retrieval, natural language processing, etc.
- AI methods applied to problems in statistics such as
statistical advisory systems, experimental design,
exploratory data analysis, causal modeling, etc.
This list is not intended to define an exclusive list of topics
of interest. Authors are encouraged to submit papers on any topic
which falls within the intersection of AI and Statistics.
SUBMISSION REQUIREMENTS:
Three copies of an extended abstract (up to 4 pages) should be sent to
David Madigan, Program Chair
6th International Workshop on AI and Statistics
Department of Statistics, Box 354322
University of Washington
Seattle, WA 98195
or electronically (postscript or latex preferred) to
aistats@stat.washington.edu
Submissions for will be considered if *postmarked* by June 30, 1996.
If the submission is electronic (e-mail), then it must be *received*
by midnight July 1, 1996.
Please indicate which topic(s) your abstract addresses and include
an electronic mail address for correspondence. Receipt of all
submissions will be confirmed via electronic mail. Acceptance
notices will be mailed by September 1, 1996. Preliminary papers (up
to 20 pages) must be returned by November 1, 1996. These preliminary
papers will be copied and distributed at the workshop.
PROGRAM COMMITTEE:
General Chair: P. Smyth UC Irvine and JPL
Program Chair: D. Madigan U. Washington
Members:
Russell Almond, ETS, Princeton
Wray Buntine, Thinkbank, Inc.
Peter Cheeseman, NASA Ames
Paul Cohen, University of Massachusetts
Greg Cooper, University of Pittsburgh
Bill DuMouchel, Columbia University
Doug Fisher, Vanderbilt University
Dan Geiger, Technion
Clark Glymour, Carnegie-Mellon University
David Hand, Open University, UK
Steve Hanks, University of Washington
Trevor Hastie, Stanford University
David Haussler, UC Santa Cruz
David Heckerman, Microsoft
Paula Hietala, University of Tampere, Finland
Geoff Hinton, University of Toronto
Mike Jordan, MIT
Hans Lenz, Free University of Berlin, Germany
David Lewis, AT&T Bell Labs
Andrew Moore, Carnegie-Mellon University
Radford Neal, University of Toronto
Jonathan Oliver, Monash University, Australia
Steve Omohundro, NEC Research, Princeton
Judea Pearl, UCLA
Daryl Pregibon, AT&T Bell Labs
Ross Shachter, Stanford University
Glenn Shafer, Rutgers University
Prakash Shenoy, University of Kansas
David Spiegelhalter, MRC, Cambridge, UK
Peter Spirtes, Carnegie-Mellon University
MORE INFORMATION:
For more information see the workshop's Web page: http://www.stat.washington.edu/aistats97/
or write David Madigan at aistats@stat.washington.edu
for
inquiries concerning the technical program or Padhraic Smyth
at aistats@jpl.nasa.gov
for other inquiries about the workshop.
Write to ai-stats-request@watstat.uwaterloo.ca
to
subscribe to the AI and Statistics mailing list.
--------
THE INSTITUTION OF ELECTRICAL ENGINEERS
PROFESSIONAL GROUP C4 (ARTIFICIAL INTELLIGENCE)
IN COLLABORATION WITH THE BRITISH COMPUTER SOCIETY
SPECIALIST GROUP ON EXPERT SYSTEMS (SGES)
COLLOQUIUM ON KNOWLEDGE DISCOVERY
LONDON, OCTOBER 17TH-18TH 1996
CALL FOR CONTRIBUTIONS
This colloquium is organised by Professional Group C4
(Artificial Intelligence) of the Institution of Electrical
Engineers, in collaboration with the British Computer Society
Specialist Group on Expert Systems (SGES) and will be held at
the IEE, Savoy Place, London WC2 on October 17th and 18th
1996.
Knowledge Discovery has been defined as 'the non-trivial
extraction of implicit, previously unknown and potentially
useful information from data'. The underlying technologies
include rule induction, case-based reasoning, genetic
algorithms, neural networks and statistics. There is a rapidly
growing body of successful applications of these and other
related technologies in a wide range of areas including
manufacturing, telecommunications, marketing, medicine and
finance.
Contributions are invited on all aspects of Knowledge
Discovery from theoretical issues through to commercial
applications.
Prospective contributors are invited to submit an extended
abstract, outlining the material they propose to present, by
Friday July 12th 1996 at the latest. Speakers will receive
free entry to the colloquium and their travel expenses will be
reimbursed by the IEE.
Abstracts should be sent either by post or by electronic mail
to the colloquium chairman:
Professor Max Bramer, Department of Information Science,
University of Portsmouth, Milton, Southsea PO4 8JF.
Tel: 01705 - 844444 Fax: 01705 - 844006
Email: bramerma@csovax.portsmouth.ac.uk
For all other information, contact:
Ms. Sarah Evans, IEE, Savoy Place, London WC2R 0BL.
Tel: 0171 - 240 - 1871 Fax: 0171-497-3633
Email: sevans@iee.org.uk
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