Implementation issues in the design of I/O intensive data mining applications on clusters of workstations

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.