New Efficient Strategy to Accelerate k-Means Clustering Algorithm
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.
How to Cite
Al- Zoubi, M. R. D. B., Hudaib, A., Huneiti, A. & Hammo, B. (2008). New Efficient Strategy to Accelerate k-Means Clustering Algorithm. American Journal of Applied Sciences, 5(9), 1247-1250. https://doi.org/10.3844/ajassp.2008.1247.1250
© 2020 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.
- k-means algorithm
- pattern recognition
- partial distance