Research Article Open Access

New Efficient Strategy to Accelerate k-Means Clustering Algorithm

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

American Journal of Applied Sciences
Volume 5 No. 9, 2008, 1247-1250
DOI: 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.

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

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Keywords

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