A New Method of Selecting K-means Initial Cluster Centers Based on Hotspot Analysis

Authors: Qu Chen*, School of Geographical Sciences, East China Normal University, Hong Yi, Department of Real Estate, East China Normal University, Yujie Hu, School of Geosciences, University of South Florida, Xianrui Xu, School of Geographical Sciences, East China Normal University, Xiang Li, School of Geographical Sciences, East China Normal University
Topics: Spatial Analysis & Modeling
Keywords: K-means algorithm, initial cluster centers, hotspot analysis, clustering
Session Type: Poster
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download


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.

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