Abstract：Clustering center cannot be automatically selected by the algorithm of fast search and find of density peaks. To solve the problem, automatic determination of clustering centers for clustering by fast search and find of density peaks is proposed. Firstly, density and distance are normalized for the problem of uneven distribution of variables, and then the upper limit of normalized density threshold is determined by Chebyshev inequality. Standard deviation is utilized to determine the upper limit of normalized distance threshold. Finally, the upper limit of decision threshold is determined according to the decision function. Two determinants are considered comprehensively to avoid the omission of the central point selection and realize the automatic determination of the cluster centers. The experiment shows that the adaptive selection of the clustering centers of the proposed algorithm is effective with good robustness and validity.
王万良, 吴菲, 吕闯. 自动确定聚类中心的快速搜索和发现密度峰值的聚类算法[J]. 模式识别与人工智能, 2019, 32(11): 1032-1041.
WANG Wanliang, WU Fei, LÜ Chuang. Automatic Determination of Clustering Center for Clustering by Fast Search and Find of Density Peaks. , 2019, 32(11): 1032-1041.
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2019, Vol. 32 
