Luca-
In spite of their appeal in the consumer world where many seem to
believe that large amounts of information is tantamount to insurance of
some type, the sorry fact is that with massive amounts of data, all too
frequently what you get is massive redundancy. David Scott's suggestion to
do mode clustering with large databases remains one of the most sensible
suggestions I've ever heard.
Regards,
Tom Ball
McKinsey & Co
55 East 52nd Street
New York, NY 10022
Luca Meyer
<lucameyer@TIS To: [log in to unmask]
CALI.IT> cc: (bcc: Thomas Ball/NYO/NorthAmerica/MCKINSEY)
Sent by: Subject: TwoStep clustering method comparison
"Classificatio
n, clustering,
and phylogeny
estimation"
<CLASS-L@lists
.sunysb.edu>
06/16/2004
11:24 AM
Please respond
to
"Classificatio
n, clustering,
and phylogeny
estimation"
Hello,
I am searching for working/published papers on twostep clustering method
comparison as well as references about this and other methods for
clustering large datasets. I am already aware of the following material:
Chiu, T., Fang, D., Chen, J., Wang, Y., and Jeris, C. (2001). A Robust
and Scalable Clustering Algorithm for Mixed Type Attributes in Large
Database Environment. Proceedings of the seventh ACM SIGKDD
international conference on knowledge discovery and data mining, 263.
Zhang, T., Ramakrishnon, R., and Livny M. (1996). BIRCH: An Efficient
Data Clustering Method for Very Large Datebases. Proceedings of the ACM
SIGMOD Conference on Management of Data, p. 103-114, Montreal, Canada.
Gore, P. A. Jr. (2000). Cluster analysis. In H. E. A. Tinsley & S. D.
Brown (Eds.), Handbook of applied multivariate statistics and
mathematical modeling (pp. 297-321). San Diego, CA: Academic Press.
Thank you in advance,
Mr. Luca Meyer
Consumer research advisor: http://www.lucameyer.com/en/
Italian Online Research Mailing List:
http://it.groups.yahoo.com/group/ior
Tel: +390122854456 - Fax: +390122854837 - Mobile: + 393355217628
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