Abstract：The clustering data analysis tool plays a significant role in various fields such as pattern recognition, bibliometrics and fault diagnosis. This paper describes a clustering approach based on neighborhood relationships, local densities and spatial grid partitions. The time complexity of this algorithm is reduced using a spatial grid with the clustering elements searched using neighborhood density relationships in the grid space. Cluster centers are then selected automatically using the maximum relative distance and the maximum relative local density. Tests on artificial data indicate that neighborhood density grid clustering can automatically cluster data and effectively process data with arbitrary shapes. Comparisons using regional recognition datasets demonstrate that this method is more suitable for clustering complex data with unusual shapes.
索明亮, 周鼎, 安若铭, 李顺利. 邻域密度网格聚类算法及应用[J]. 清华大学学报（自然科学版）, 2018, 58(8): 732-739.
SUO Mingliang, ZHOU Ding, AN Ruoming, LI Shunli. Neighborhood density grid clustering and its applications. Journal of Tsinghua University(Science and Technology), 2018, 58(8): 732-739.
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