作者: R. Baraglia , D. Laforenza , Salvatore Orlando , P. Palmerini , Raffaele Perego
关键词: Knowledge acquisition 、 Input/output 、 Information system 、 Data mining 、 Computer science 、 Parallel algorithm 、 Implementation 、 Cluster analysis 、 Workstation 、 Scalability
摘要: This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms on affordable parallel architectures, such as clusters w orkstations. In order to validate our approach, the K-means algorithm, a well known DM Clustering was used test case.