This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms on affordable parallel architectures, such as clusters of workstations. In order to validate our approach, the K-means algorithm, a well known DM Clustering algorithm, was used as a test case.

Implementation Issues in the Design of I/O Intensive Data Mining Applications on Clusters of Workstations

ORLANDO, Salvatore;
2000

Abstract

This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms on affordable parallel architectures, such as clusters of workstations. In order to validate our approach, the K-means algorithm, a well known DM Clustering algorithm, was used as a test case.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/23346
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