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2000
IEEE

Scalable Parallel Clustering for Data Mining on Multicomputers

10 years 8 months ago
Scalable Parallel Clustering for Data Mining on Multicomputers
This paper describes the design and implementation on MIMD parallel machines of P-AutoClass, a parallel version of the AutoClass system based upon the Bayesian method for determining optimal classes in large datasets. The P-AutoClass implementation divides the clustering task among the processors of a multicomputer so that they work on their own partition and exchange their intermediate results. The system architecture, its implementation and experimental performance results on different processor numbers and dataset sizes are presented and discussed. In particular, efficiency and scalability of P-AutoClass versus the sequential AutoClass system are evaluated and compared.
D. Foti, D. Lipari, Clara Pizzuti, Domenico Talia
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where IPPS
Authors D. Foti, D. Lipari, Clara Pizzuti, Domenico Talia
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