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Free Parallel Data Mining

Bin Li, Dennis Shasha

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Abstract

Data mining is computationally expensive. Since the benefits of data mining results are unpredictable, organizations may not be willing to buy new hardware for that purpose. We will present a system that enables data mining applications to run in parallel on networks of workstations in a fault-tolerant manner. We will describe our parallelization of a combinatorial pattern discovery algorithm and a classification tree algorithm. We will demonstrate the effectiveness of our system with two real applications: discovering active motifs in protein sequences and predicting foreign exchange rate movement.

References

References, where available, link to the DBLP on the World Wide Web.

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Jason Tsong-Li Wang, Gung-Wei Chirn, Thomas G. Marr, Bruce A. Shapiro, Dennis Shasha, Kaizhong Zhang: Combinatorial Pattern Discovery for Scientific Data: Some Preliminary Results. SIGMOD Conference 1994: 115-125

BIBTEX

@inproceedings{DBLP:conf/sigmod/LiS98,
author = {Bin Li and
Dennis Shasha},
editor = {Laura M. Haas and
Ashutosh Tiwary},
title = {Free Parallel Data Mining},
booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
publisher = {ACM Press},
year = {1998},
isbn = {0-89791-955-5},
pages = {541-543},
crossref = {DBLP:conf/sigmod/98},
bibsource = {DBLP, http://dblp.uni-trier.de}
}