Research Article Open Access

New Efficient Strategy to Accelerate k-Means Clustering Algorithm

Moh’d Belal Al- Zoubi1, Amjad Hudaib1, Ammar Huneiti1 and Bassam Hammo1
  • 1 ,
American Journal of Applied Sciences
Volume 5 No. 9, 2008, 1247-1250

DOI: https://doi.org/10.3844/ajassp.2008.1247.1250

Submitted On: 8 December 2007 Published On: 30 September 2008

How to Cite: Al- Zoubi, M. 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

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.

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Keywords

  • clustering
  • k-means algorithm
  • pattern recognition
  • partial distance