Research on automatic determining clustering centers algorithm based on linear regression analysis
Guo Pengcheng, Xing Wang, Wang Yubing, Yue Cheng, Ying Zhang · 2017
Regarding the deficiencies of clustering algorithm with fast method of searching and finding density peaks which is published in Science in 2014, an automatically and fast finding of clustering centers clustering algorithm is proposed, which applies linear regression and residual analysis and optimizes sample density value. The proposed algorithm improves location stability of clustering centers by measuring point density through sample's nearest neighbors information, and it determines clustering centers fast and automatically by applying linear regression and residual analysis, which reduces the subjectivity of artificial selection. Theoretical analysis and simulation results show that the proposed algorithm can overcome deficiencies of the original algorithm, what's more, the results of clustering effect and calculation time is superior to original algorithm, K-means and DBSCAN.