[R] please advise re: data mining in Germany

[Lisa Solomon]

     Our CEO, Dr. Dan Steinberg, is planning to visit Germany in September.
He would like the opportunity to introduce statisticians (and 
statistically minded
people) to data mining, data mining applications and to forefront data
mining tools.  Our algorithms are probably familiar to many
statisticians (CART, MARS and MART), although it isn't necessary to be a
statistician to use our tools.  We co-develop with Jerome Friedman of
Stanford University and Leo Breiman of Berkeley.
     I am trying to locate people/companies in Germany who would
benefit from data mining.  If you know of any, I would appreciate it if
you would let me know.
     Below is information about Salford Systems, Dr. Steinberg and the
abstract of a recent presentation.

Sincerely,
Lisa Solomon
lisas at salford-systems.com <mailto:lisas at salford-systems.com>
http://www.salford-systems.com?germany
001-619-543-8880 x21

Salford Systems Background:
Founded in 1983, Salford Systems specializes in providing new generation
data mining and choice modeling software and consultation services.
Applications in both software and consulting span market research
segmentation, direct marketing, fraud detection, credit scoring, risk
management, bio-medical research and manufacturing quality control.
Industries using Salford Systems products and consultation services
include telecommunications, transportation, banking, financial services,
insurance, health care, manufacturing, retail and catalog sales, and
education.  Salford Systems software is installed at more than 3,500
sites worldwide, including 300 major Universities.  Key customers
include AT&T Universal Card Services, Pfizer Pharmaceuticals, General
Motors, Sears, and Roebuck and Co.

Dan Steinberg Background:
Dan Steinberg is a well respected member of the data mining, statistics
and analytical consultation communities.  His more than 20 years of
experience in the field include Member of Technical Staff at AT&T Bell
Laboratories,  and Assistant Professor of Econometrics at the University
of California, San Diego, as well as numerous consultation engagements
with Fortune 100 clients.  He received his Ph.D. in Economics from
Harvard University, and he has received honors from the SAS User's Group
International.  Steinberg has published articles in statistics,
econometrics, computer science, and marketing journals, and he is the
developer of a series of advanced statistical analysis programs.  In
addition, he has been a featured data mining issues speaker for the
American Marketing Association, American Statistical Association and the
Direct Marketing Association

Recent Presentation to San Antonio Chapter American Statistical Society:
Data Mining in Practice:
Banking, Insurance, Bioinformatics


Abstract

Part I:  Data mining is the application of modern, highly automated
nonparametric analytical methods to recognize enduring patterns in data.
  The methods can produce a variety of models for a variety of
industries. Models include classification schemes, regressions, and
finding clusters of objects and attributes.  This review will focus on
classification problems, including, recognizing customers who are most
at risk of switching to a competitor, recognizing loan applicants most
likely to default, and recognizing unsafe product designs. Most data
mining problems are fundamentally minor variations on just a few key
themes and we will use the examples to illustrate this point.

Part II:  Data mining methods have been experimented with for some time
in the bioinformatics world, originally being applied to the problems of
drug discovery, selection of patients into clinical trials, and
epidemiological studies. In the last year or so the number of
researchers using data mining methods has expanded considerably, and the
range of methods has expanded as well.  In particular, CART has been
used extensively in the analysis of proteomics and genomics data, and
Treenet stochastic gradient boosting has been quite successful in the
analysis of DNA microarray data.