ST-CFSFDP algorithm based on Euclidean distance constraint

The spatial-temporal clustering by fast search and find of density peaks (ST-CFSFDP) has a better clustering effect on the spatiotemporal data set in a small space. However, there are some deficiencies in the spatiotemporal dataset with large data volume and far interval between sample points, the clustering results showed great differences, too many interference points during visualization. Given the above deficiencies, this paper proposes a spatial-temporal clustering by fast search and find of density peak algorithm based on Euclidean distance constraint, by increasing the partition constraint of some sample points, the problems existing in the spatiotemporal clustering algorithm of ST-CFSFDP are improved. Experimental results show that the improved algorithm has a better clustering effect than the original algorithm.