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A Data-Driven Parameter Adaptive Clustering Algorithm Based on Density Peak
Clustering is an important unsupervised machine learning method which can efficiently partition points without training data set. However, most of the existing clustering algorithms need to set parameters artificially, and the results of clustering are much influenced by these parameters, so optimizing clustering parameters is a key factor of improving clustering performance. In this paper, we propose a parameter adaptive clustering algorithm DDPA-DP which is based on density-peak algorithm. Indoi:10.1155/2018/5232543 fatcat:pnhy3q7m5bbefilnl5rcfb2prq