--
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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
To be uncertain is uncomfortable, to be certain is ridiculous.
Chinese proverb. Thanks to Brij Masand, brij@gte.com
Previous1NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Wed, 25 Sep 1996 10:42:32 -0400
From: Dawn Cohen (cohend@war.wyeth.com)
Subject: Nuggets 96:30 -Reply
Hi, Gregory--
In response to your KDD-Nuggets posting of the article on P-Trak, I'd like
to direct readers to the discussion that has been taking place on
comp.risks.
It appears that (contrary to the article's claim), SSN's have NOT been
pulled. They are just not visible (printable?) In particular, if you have a
SSN that you'd like to search for, and bring up a name and address for,
you can do it. So if I have access to a SSN indexed database that is
missing names and addresses, I can still use my access to P-Trak to
look up the corresponding name/address/phone information. What I can't
do, using just P-Trak (apparently...I don't actually have access to it) is get
a listing of names, addresses and social security numbers.
--Dawn
Previous2NextTop
>~~~Publications:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Tue, 01 Oct 1996 17:01:47 -0400
From: Gregory Piatetsky-Shapiro (gps@gte.com)
Subject: IEEE Expert: Special Issue on Data Mining Applications -
October 1996: Contents
see http://www.computer.org/pubs/expert/toc
here are the DM-related articles.
-- GPS
-----------------
Guest Editor's Introduction: Data mining-Here We Go Again?
Bill Mark
Data Mining and Knowledge Discovery: Making Sense Out of Data
Usama M. Fayyad
Reality Check for Data Mining
Evangelos Simoudis
Mining Geophysical Data for Knowledge
Edmond Mesrobian, Richard Muntz, Eddie Shek, Siliva Nittel,
Mark La Rouche, Marc Kriguer, Carlos Mechoso, John Farrara, Paul
Stolorz, Hisashi Nakamura
Constructing Bayesian Networks to Predict Uncollectible
Telecommunications Accounts
Kazuo J. Ezawa and Steven W. Norton
Stock Selection Using Rule Induction
George H. John, Peter Miller, Randy Kerber
Scalable Discovery of Informative Structural Concepts Using Domain Knowledge
Diane J. Cook, Lawrence B. Holder, and Surnjani Djoko
Visualization Support for Data Mining
Hing-Yan Lee and Hwee-Leng Ong
Executive Insight
Searching for the mother lode: tales of the first data miners
DATABASE MARKETING PREDICTS CUSTOMER LOYALTY, p. 50-58
It Costs More to Find new Customers Than It Does to Keep the Ones You Already
Have. Database Marketing Can Help You Prevent Customer Turnover From Churning
Away Profits.
' ... Database marketing combines data mining with predictive modeling and
desktop presentation tools to help marketers develop sharper insights into
customer behavior. The ultimate goal is to predict customer loyalty ...'
It costs more to find new customers than it does to keep the
ones you already have. Database marketing can help you
prevent customer turnover from churning away profits.
By Sarah E. Varney
Here's the next challenge for your data warehouse: Customer
retention. You need to find a way to let marketing use the data they
collect about customers to predict future buying behavior. That's
where database marketing comes in.
Database marketing combines data mining with predictive modeling and
desktop presentation tools to help marketeers develop sharper insights
into customer behavior. The ultimate goal is to predict customer
loyalty--to learn which customers are likely to stay true to your
brand and which customer segments are most at risk to jump ship. If
you can predict those behaviors, your company has a better chance of
keeping customers loyal.
Why is lowering customer turnover--or churn--so important? It's
cheaper to keep customers loyal than to find new ones to replace
them. That's especially true in the telecom and wireless
communications sectors. Deregulation in telecommunications and in
utilities means more choices for customers; and more choices for
customers means a higher risk of churn for the company and higher
marketing costs.
At Southern California Gas Co., deregulation was the impetus for
starting a database marketing program. 'Before deregulation, we
didn't have a marketing department,' says Tom Eunice, the
consultant who oversees database marketing for the Los
Angeles-based company. The utility kept its mainframes and DB2
database and modified its existing data analysis tools to sharpen its
marketing focus. Over the past two years, the gas company has saved
about $400,000 in direct marketing costs by using data mining
techniques to predict customer behavior. Using database marketing
techniques, the marketing department was able to delineate a
segment of customers most likely to sign up for a level payment
plan. 'We used SAS to figure out which customers would want it. We
focused on those customers with the greatest summer-to-winter
variance [in utility bills], who are probably single-family home
owners. Then we built a model and ran it against every customer. It
kicked out the most likely customers, and we direct marketed to
them,' explains Eunice. The response rate was
phenomenal--between 7 and 11%. 'That's incredibly high for direct
marketing,' Eunice says.
The company uses SAS Institute's suite of tools to analyze customer
information culled from a datamart created from information extracted
from the company's billing records and combined with credit data from
Equifax and U.S. Census records. The system allows marketers to pull
data from the company's DB2 repository, along with VSAM and flat file
records from the mainframe. An HP 9000 server running the SAS System
takes care of the statistical analysis tasks.
Southern California Gas has also used its database marketing system to
decrease churn. Recently the company learned that small, heating-only
commercial customers were more sensitive to price increases than
anyone had imagined. 'We found out that if we raise their rates,
they'll switch to electric, mostly in the form of space heaters,' says
Eunice. After learning this, the company did a cost/benefit
analysis. 'We wanted to know: How much does it cost us to have them
leave? And we decided that we weren't losing enough to spend a heck of
a lot of money on them. But we changed how we market to them. They now
get different literature,'' that addresses the issue.
Integrate to implement
The key to successfully implementing database marketing is to
integrate three main components, according to Stephen Husan, product
manager at Lightbridge Inc., a Cambridge, Mass.-based consulting firm
that specializes in building and integrating customer retention apps
for the wireless sector. Those three components are:
The actual data stored in the mart or warehouse--much of it
culled from billing systems
Statistical techniques or tools using predictive modeling
Sophisticated presentation tools.
The goal is to implement a process that generates accurate customer
segments. Once a data-mining engine has culled an appropriate group of
customer segments from the warehouse, the next step is to further
extrapolate accurate customer profiles. These profiles are supposed to
reflect which customers are most at risk for switching to a rival
product or service. With each new round of customer data, a new set of
statistical models is created and run against the segments.
Understanding these predictive models and how to implement them isn't
easy. The reality is that most IS shops aren't prepared to tackle this
kind of project alone, says Lightbridge's Husan. 'Most IS managers
don't think that they can build it themselves. In this realm, where
you're integrating a data warehouse with data mining, you really have
to understand predictive modeling.'
Lightbridge currently sells a package called Channel Wizard, which
helps companies discern which channels are selling the most and how to
alter marketing plans to increase sales.
The company has just begun selling Churn Prophet, a database marketing
package that includes predictive capabilities based on Pilot
Software's Discovery Server, which uses predictive sampling techniques
to comb through data warehouses for information on customer
segmentation. Both products are aimed at wireless companies, where
churn is a life or death issue. 'In the wireless industry, the
individual churn rate is at 25% annually. That means they lose 25% of
their customer base every year,' says Husan.
Fierce wireless competition
With deregulation and the licensing of the PCS airwaves, there will be
five to seven carriers for consumers to choose from, rather than just
two. And when you consider the very high acquisition costs--because
the phone itself is generally included--it's easy to see why both the
telecom companies and the wireless providers are facing a competitive
feeding frenzy.
Lightbridge is currently working on a large project for Southwestern
Bell Mobile Systems that will integrate Churn Prophet. About a year
and a half ago, the company started a data warehouse project using
Oracle database software and Pyramid Technology's SMP hardware.
The regional Bell operating company is using a Nile SMP system now
and plans to move to an RM 1000 system soon. Ultimately, the data
warehouse will hold about a terabyte of data. The goal is to roll out
these database marketing apps (based on subscriber information) to
about 100 marketeers at Southwestern Bell, over the next six
months.
Wireless isn't the only industry beset by deregulation woes. If you
manage IS for a bank, your life is already pretty topsy-turvy.
Deregulation in banking has led to merger mania--and mergers are
a nightmare for IS. Some banks are using database marketing to
determine the credit worthiness of their customers.
At Corestates Bank, a $45 billion regional bank holding company based
in Philadelphia, database marketing is in the early stages. The
company plans to start rolling out predictive modeling tools for its
marketing group during the first and second quarter of next year, says
Kim Foster, assistant vice president of datawarehousing technology and
retail credit policy.
The retail credit bank is beginning to build an enterprisewide data
warehouse. Simultaneously, Foster's site is building a DB2-based
datamart that will house about 500GB of data when complete. Currently,
the department uses its Retail Credit Information System (RCRIS) to
slice and dice customer and portfolio data. RCRIS was built using SAS
Institute's tool suite. Corestates' marketing group uses the system to
analyze credit risks of certain customer groups. 'If a marketing group
does a pre-approval program, and doesn't analyze the customer group
properly, you wouldn't know they were bad risks until three to six
months down the road,' says Foster, explaining the importance of
accurate analysis at Corestates.
In addition to the warehousing project, Foster's IS department is also
working on a GIS application based on SAS GIS that will let Corestates
visualize lending patterns as loan data is added to the central data
store. This will help the bank make sure that it is always in
compliance with federal and state lending requirements, adds Foster.
A peek inside the toolbox
Some new and existing software packages enable less-complex database
marketing apps. These shrink-wrapped offerings include CRM Marketing
Database 2.0 from S2 Systems, Arbor/BP from Kenan Systems, and Holos 5.0
from Holistic Systems.
S2's CRM Marketing Database 2.0 works with Informix On-Line Dynamic
Server 7.1 and supports customer profiling and retention
tracking. Kenan Systems' Arbor/BP is a billing and customer care
system for UNIX; it includes a set of APIs that allows IS users to
integrate add-ons and custom business rules.
Holistic Systems' Holos 5.0 is a development environment for IS to
build applications, but it includes advanced agent technology combined
with predictive modeling and support for data mining, making itideal
for database marketing users, says Robin Cortiss, vice president of
technology for the Edison, N.J., company. The package also includes
neural network technology that allows it to more intelligently mine
for data.
Users can configure agents to alert them to changes in data or to
trigger processes back on the database or data warehouse server. For
example, a marketer who discovers that a particular customer segment
is ripe to buy into a new rate plan could use agents to mark those
individual customers and set up a mailing label process for a direct
marketing campaign.
Even if your company doesn't do business in any of the fields we've
mentioned, chances are that increased competition will boost interest
in customer loyalty. Often the marketing department isn't aware of the
importance of customer loyalty until it makes one important
calculation: 'A lot of companies haven't figured out what it costs
them to acquire a new customer,' says Coopers & Lybrand's Vince
Bowey. ''It's usually pretty shocking. We estimate that it costs three
to five times more money to acquire a new customer than to keep the
ones you have.''
Once companies make that calculation, it doesn't take them long to
figure out that the next step is to use a lifetime value model to
determine what a customer will spend over a certain time period. 'It's
a natural next step,' says Bowey.
Comments: Software modules are customized for decision makers who have large databases (up to 2000 variables) and need tools for reasoning from data. The tools assist in tasks like modelling, forecasting, expert system building, design and intelligent decision support (see URL).
Platforms: MS-DOS, Windows, Unix
Contact: REDUCT & Lobbe Technologies Inc.
Suite 402, 4010 Pasqua Street
Regina, Saskatchewan, Canada S4P 3L7
tel: (306) 586-9400; fax: (306) 586-9442
email: reduct@sasknet.sk.ca
Status: Products, Services
Updated: 1996-09-25 Previous5NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Return-Path: (gps0@gte.com)
X-Authentication-Warning: relay.gte.com: postman set sender to (singlis@venus.cs.waikato.ac.nz)
using -f
Date: Tue, 01 Oct 1996 10:07:06 +1200
From: Stuart Inglis (singlis@venus.cs.waikato.ac.nz)
Subject: WEKA
To: kdd@gte.com
Cc: singlis@lucy.cs.waikato.ac.nz
Content-transfer-encoding: 7BIT
Content-Length: 1322
The WEKA Machine Learning workbench
-----------------------------------
WEKA* is now available from http://www.cs.waikato.ac.nz/~ml
for downloading
and experimentation. WEKA is a software workbench for applying machine
learning techniques to practical problems. It integrates many different
machine learning tools within a common framework and a uniform user
interface. It runs on a Unix/X system with Tcl7.5/Tk4.1 and BLT
extensions.
WEKA includes
Uniform user interface
Tutorial
1R and T2 programs for simple rules
Induct program for more complex rules
IB1-4, PEBLS and K* programs for instance-based learning
M5' program for regression model trees
FOIL program for relational rules
Sample data sets in WEKA format
Programs for processing WEKA data files
Rule evaluator.
It can be extended by adding modules (which need additional software and
licences) for
C4.5 and tree visualization
Classweb clustering program
More comprehensive rule evaluator
Attribute and experiment editors.
* WEKA stands for Waikato Environment for Knowledge Analysis. Found only
on the islands of New Zealand, the weka is a flightless bird with an
inquisitive nature.
Previous6NextTop
>~~~Meetings:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Subject: Call for Participation for AISTATS-97
Date: Fri, 20 Sep 1996 13:53:19 -0700
From: Padhraic Smyth (smyth@galway.ICS.UCI.EDU)
Call for Participation
SIXTH INTERNATIONAL WORKSHOP ON
ARTIFICIAL INTELLIGENCE AND STATISTICS
January 4-7, 1997
Ft. Lauderdale, Florida
REGISTRATION AND CONFERENCE INFORMATION
Conference and tutorial registration forms, hotel information,
and descriptions of the technical program and tutorials are all
now available from the conference Web site:
The workshop will be held at the Radisson Bahia Mar Beach Resort
in Fort Lauderdale. The workshop will consist of 20 plenary talks
and 38 poster presentations over 2 1/2 days (January 5th to 7th).
Preceding the workshop, January 4th, will be a day of tutorials by
A. P. Dawid (University College London), Mike Jordan (MIT),
Tom Mitchell (CMU), and Mike West (Duke University).
Attendance at the workshop is open to all and is *not* limited
to presenters of papers. Register early by December 2nd to take
advantage of reduced registration fees.
MORE INFORMATION:
Visit the website directly for online information. For further details
write to David Madigan (Program Chair) at aistats@stat.washington.edu
for inquiries concerning the technical program or Padhraic Smyth
(General Chair) at aistats@aig.jpl.nasa.gov
for other inquiries
about the workshop.
Previous7NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Marney Smyth (marney@ai.mit.edu)
Subject: Modern Regression and Classification Course in Boston
Date: Wed, 25 Sep 1996 21:36:53 -0400 (EDT)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+++ +++
+++ Modern Regression and Classification +++
+++ Widely Applicable Statistical Methods for +++
+++ Modeling and Prediction +++
+++ +++
+++ Cambridge, MA, December 9 - 10, 1996 +++
+++ +++
+++ Trevor Hastie, Stanford University +++
+++ Rob Tibshirani, University of Toronto +++
+++ +++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
This two-day course will give a detailed overview of statistical
models for regression and classification. Known as machine-learning in
computer science and artificial intelligence, and pattern recognition
in engineering, this is a hot field with powerful applications in
science, industry and finance.
The course covers a wide range of models, from linear regression
through various classes of more flexible models, to fully
nonparametric regression models, both for the regression problem and
for classification. Although a firm theoretical motivation will be
presented, the emphasis will be on practical applications and
implementations. The course will include many examples and case
studies, and participants should leave the course well-armed to tackle
real problems with realistic tools. The instructors are at the
forefront in research in this area.
After a brief overview of linear regression tools, methods for
one-dimensional and multi-dimensional smoothing are presented, as well
as techniques that assume a specific structure for the regression
function. These include splines, wavelets, additive models, MARS
(multivariate adaptive regression splines), projection pursuit
regression, neural networks and regression trees.
The same hierarchy of techniques is available for classification
problems. Classical tools such as linear discriminant analysis and
logistic regression can be enriched to account for nonlinearities and
interactions. Generalized additive models and flexible discriminant
analysis, neural networks and radial basis functions, classification
trees and kernel estimates are all such generalizations. Other
specialized techniques for classification including nearest-neighbor
rules and learning vector quantization will also be covered.
Apart from describing these techniques and their applications to a
wide range of problems, the course will also cover model selection
techniques, such as cross-validation and the bootstrap, and diagnostic
techniques for model assessment.
Software for these techniques will be illustrated, and a comprehensive
set of course notes will be provided to each attendee.
Additional information is available at the Website:
Overview of regression methods: Linear regression models and least
squares. Ridge regression and the lasso. Flexible linear models
and basis function methods. linear and nonlinear smoothers; kernels,
splines, and wavelets. Bias/variance tradeoff- cross-validation and
bootstrap. Smoothing parameters and effective number of parameters.
Surface smoothers.
++++++++
Structured Nonparametric Regression: Problems with high dimensional
smoothing. Structured high-dimensional regression: additive models.
project pursuit regression. CART, MARS. radial basis functions.
neural networks. applications to time series forecasting.
DAY TWO:
Classification: Statistical decision theory and classification rules.
Linear procedures: Discriminant Analysis. Logistics regression.
Quadratic discriminant analysis, parametric models. Nearest neighbor
classification, K-means and LVQ. Adaptive nearest neighbor methods.
Professor Trevor Hastie of the Statistics and Biostatistics
Departments at Stanford University was formerly a member of the
Statistics and Data Analysis Research group AT & T Bell
Laboratories. He co-authored with Tibshirani the monograph Generalized
Additive Models (1990) published by Chapman and Hall, and has many
research articles in the area of nonparametric regression and
classification. He also co-edited the Wadsworth book Statistical
Models in S (1991) with John Chambers.
Professor Robert Tibshirani of the Statistics and Biostatistics
departments at University of Toronto is the most recent recipient of
the COPSS award - an award given jointly by all the leading
statistical societies to the most outstanding statistician under the
age of 40. He also has many research articles on nonparametric
regression and classification. With Bradley Efron he co-authored the
best-selling text An Introduction to the Bootstrap in 1993, and has
been an active researcher on bootstrap technology for the past 11
years.
Quotes from previous participants:
'... the best presentation by professional statisticians I have ever
had the pleasure of attending'
'.. superior to most courses in all respects.'
Both Prof. Hastie and Prof. Tibshirani are actively involved in
research in modern regression and classification and are well-known
not only in the statistics community but in the machine-learning and
neural network fields as well. The have given many short courses
together on classification and regression procedures to a wide variety
of academic, government and industrial audiences. These include the
American Statistical Association and Interface meetings, NATO ASI
Neural Networks and Statistics workshop, AI and Statistics, and the
Canadian Statistical Society meetings.
BOSTON COURSE: December 9-10, 1996 at the
HYATT REGENCY HOTEL, CAMBRIDGE, MASSACHUSETTS.
PRICE: $750 per attendee before November 11, 1996. Full time
registered students receive a 40% discount (i.e. $450). Cancellation
fee is $100 after October 29, 1996. Registration fee after November
11, 1996 is $950 (Students $530). Attendance is limited to the first
60 applicants, so sign up soon! These courses fill up quickly.
HOTEL ACCOMMODATION
The Hyatt Regency Hotel offers special accommodation rates for course
participants ($139 per night). Contact the hotel directly -
The Hyatt Regency Hotel, 575 Memorial Drive, Cambridge, MA 02139.
Phone : 617 4912-1234
Alternative hotel accommodation information at MRC WebSite:
__________________________________________________________
Credit card # (if payment by credit card) Expiration Date
(Lunch Menu - tick as appropriate):
___ Vegetarian ___ Non-Vegetarian
Fee payment must be made by MONEY ORDER, PERSONAL CHECK, VISA or
MASTERCARD. All amounts must in US dollar figures. Make fee payable
to Prof. Trevor Hastie. Mail it, together with this completed
Registration Form to:
Marney Smyth,
MIT Press
E39-311
55 Hayward Street,
Cambridge, MA 02142 USA
ALL CREDIT CARD REGISTRATIONS MUST INCLUDE BOTH CARD NUMBER AND
EXPIRATION DATE.
DEADLINE: Registration before December 2, 1996. DO NOT SEND CASH.
Registration fee includes Course Materials, coffee breaks,
and lunch both days.
If you have further questions, email to marney@ai.mit.edu
Previous8NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Wed, 2 Oct 96 00:13:24 PDT
From: freeman@systems.caltech.edu
(Robert Freeman)
Subject: Conference: Neural Networks in the Capital Markets 9/20/96
Neural networks have been applied to a number of live systems in the
capital markets, and in many cases have demonstrated better performance
than competing approaches. Because of the increasing interest in the NNCM
conferences held in the U.K. and the U.S., the fourth annual NNCM will be
held on November 20-22, 1996, in Pasadena, California. This is a research
meeting where original and significant contributions to the field are
presented. A day of tutorials (Wednesday, November 20) is included to
familiarize audiences of different backgrounds with some of the key
financial and mathematical aspects of the field.
Invited Speakers:
The conference will feature invited talks by three internationally
recognized researchers:
Dr. Rob Engle, UC San Diego
Dr. Andrew Lo, MIT Sloan School
Dr. Paul Refenes, London Business School
Contributed Papers:
NNCM-96 will have 4 oral sessions and 2 poster sessions with more than 40
contributed papers presented by academicians and practitioners from all six
continents, both from the neural networks side and the capital markets
side. Each paper has been refereed
by 3 experts in the field. The areas of the accepted papers include price
forecasting for stocks, bonds, commodities, and foreign exchange; asset
allocation and risk management; volatility analysis and pricing of
derivatives; cointegration, correlation, and multivariate data analysis;
credit assessment and economic forecasting; statistical methods, learning
techniques, and hybrid systems.
Tutorials:
Before the main program, there will be a day of tutorials on Wednesday,
November 20, 1996. Three two-hour tutorials will be presented as follows:
Statistical Models of Financial Volatility
Dr. Rob Engle, University of California, San Diego
Universal Portfolios and Information Theory
Dr. Tom Cover, Stanford University
Data-Snooping and Other Selection Biases in Financial Econometrics
Dr. Andrew Lo, MIT Sloan School
We are very pleased to have tutors of such caliber help bring new audiences
from different backgrounds up to speed in this cross-disciplinary area.
Schedule Outline:
Wednesday, November 20: 9:00- 5:30 Tutorials 1, 2, 3
Thursday, November 21: 8:30-11:30 Oral Session I
11:30- 2:00 Luncheon & Poster Session I
2:00- 5:00 Oral Session II
Friday, November 22: 8:30-11:30 Oral Session III
11:30- 2:00 Luncheon & Poster Session II
2:00- 5:00 Oral Session IV
Organizing Committee:
Dr. Y. Abu-Mostafa, Caltech (Chairman)
Dr. A. Atiya, Cairo University
Dr. N. Biggs, London School of Economics
Dr. D. Bunn, London Business School
Dr. M. Jabri, Sydney University
Dr. B. LeBaron, University of Wisconsin
Dr. A. Lo, MIT Sloan School
Dr. I. Matsuba, Chiba University
Dr. J. Moody, Oregon Graduate Institute
Dr. C. Pedreira, Catholic Univ. PUC-Rio
Dr. A. Refenes, London Business School
Dr. M. Steiner, Universitaet Augsburg
Dr. A. Timmermann, UC San Diego
Dr. A. Weigend, University of Colorado
Dr. H. White, UC San Diego
Dr. L. Xu, Chinese University of Hong Kong
Location:
The conference will be held at the Ritz-Carlton Huntington Hotel in
Pasadena, within two miles from the Caltech campus. One of the most
beautiful hotels in the U.S., the Ritz is a 35-minute drive from Los
Angeles International Airport (LAX) with nonstop flights from
most major cities in North America, Europe, the Far East, Australia, and
South America.
Home of Caltech, Pasadena has recently become a major dining/hangout center
for Southern California with the growth of its `Old Town', built along the
styles of the 1950's. Among the cultural attractions of Pasadena are the
Norton Simon Museum, the Huntington
Library/Gallery/Gardens, and a number of theaters including the Ambassador
Theater.
Hotel Reservation:
Please contact the Ritz-Carlton Huntington Hotel in Pasadena directly. The
phone number is (818) 568-3900 and the fax number is (818) 568-1842. Ask
for the NNCM-96 rate. We have negotiated an (incredible) rate of $79+taxes
($110 with $31 credited by NNCM-96 upon registration) per room (single or
double occupancy) per night. Please make the hotel reservation IMMEDIATELY
as the rate is based on availability.
Registration:
Registration is done by mail on a first-come, first-served basis. To
ensure your place at the conference, please send the following registration
form and payment as soon as possible to
Ms. Lucinda Acosta, Caltech 136-93, Pasadena, CA 91125, U.S.A.
********Please circle the applicable fees and write the total below********
Main Conference (November 21-22):
Registration fee $550
Discounted fee for academicians $275
(letter on university letterhead required)
Discounted fee for full-time students $150
(letter from registrar or faculty advisor required)
Tutorials (November 20):
You must be registered for the main conference in order to register for the
tutorials.
Tutorials Fee $150
Full-time students $100
(letter from registrar or faculty advisor required)
TOTAL: $_________
Please include payment (check or money order in US currency). Please make
check payable to Caltech. Mail your completed registration form and payment
to
Ms. Lucinda Acosta, Caltech 136-93, Pasadena, CA 91125, U.S.A.
There is shuttle service (around $25 per person) and bus service (around
$15 per person) from Los Angeles International Airport (LAX) to the
Ritz-Carlton Hotel in Pasadena. A taxi ride will cost approximately $55.
There is also shuttle service from Burbank airport (BUR) which is a
domestic airport closer to Pasadena. A taxi ride will cost approximately
$35.
Secretariat:
For further information, please contact the NNCM-96 secretariat:
KDD Nuggets Index