American Journal of Applied Sciences

New Efficient Strategy to Accelerate k-Means Clustering Algorithm

Moh’d Belal Al- Zoubi, Amjad Hudaib, Ammar Huneiti and Bassam Hammo

DOI : 10.3844/ajassp.2008.1247.1250

American Journal of Applied Sciences

Volume 5, Issue 9

Pages 1247-1250

Abstract

One of the most popular clustering techniques is the k-means clustering algorithm. However, the utilization of the k-means is severely limited by its high computational complexity. In this study, we propose a new strategy to accelerate the k-means clustering algorithm through the Partial Distance (PD) logic. The proposed strategy avoids many unnecessary distance calculations by applying efficient PD strategy. Experiments show the efficiency of the proposed strategy when applied to different data sets.

Copyright

© 2008 Moh’d Belal Al- Zoubi, Amjad Hudaib, Ammar Huneiti and Bassam Hammo. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.