This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms on affordable parallel architectures, such as clusters of w orkstations. 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
Baraglia R;Laforenza D;Orlando S;Palmerini P;Perego R
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 w orkstations. In order to validate our approach, the K-means algorithm, a well known DM Clustering algorithm, was used as a test case.File in questo prodotto:
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