TY - JOUR AU - Al- Zoubi, Moh’d Belal AU - Hudaib, Amjad AU - Huneiti, Ammar AU - Hammo, Bassam PY - 2008 TI - New Efficient Strategy to Accelerate k-Means Clustering Algorithm JF - American Journal of Applied Sciences VL - 5 IS - 9 DO - 10.3844/ajassp.2008.1247.1250 UR - https://thescipub.com/abstract/ajassp.2008.1247.1250 AB - 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.