Parallel k/h-Means Clustering for Large Data Sets

This paper describes the realization of a parallel version of the k/h-means clustering algorithm. This is one of the basic algorithms used in a wide range of data mining tasks. We show how a database can be distributed and how the algorithm can be applied to this distributed database. The tests conducted on a network of 32 PCs showed for large data sets a nearly ideal speedup.

• Type: Conference
• Year: 1999
• Where: EUROPAR
• Authors: Kilian Stoffel, Abdelkader Belkoniene
• Keywords: Data Mining Tasks, Distributed And Parallel Computing, EUROPAR 1999, K/h-means Clustering Algorithm, Parallel Version