An efficient clustering method for medical data applications

Clustering task is aimed at classifying elements into clusters, which is applied to different fields of the human activity. In this paper, an efficient clustering method by fast search and find of density peaks (FSFDP) is used for medical data applications. Different computing methods of the local density are compared and analyzed. For datasets composed by a small number of points, the local density might be affected by large statistical errors. Kernel local density is more accurate for estimating the density. Experiments were conducted to validate the efficiencies of the clustering method based on different local density for UCI benchmark and real-life datasets. The results show the feasibility and efficiency of the method for medical data clustering analysis.