A New Method of Selecting K-means Initial Cluster Centers Based on Hotspot Analysis
Qu Chen, Hong Yi, Yujie Hu, Xianrui Xu, Xiang Li · 2018
The initial cluster centers of traditional K-means algorithm are randomly selected from spatially distributed data samples. This procedure may significantly affect the final clustering outputs and is unable to ensure a high-quality solution. This paper attempts to improve the quality of solution and the efficiency of clustering through selecting initial cluster centers based on hotspot analysis. An algorithm is developed to identify$K$hotspots as initial cluster centers. The proposed algorithm is compared to three existing methods in our experiments. Our results demonstrate that our method can generate similar but more stable clustering results with less number of iterations than others.