Rapid clustering method based on density sub-graph estimation, computer equipment and storage medium

The invention relates to the technical field of machine learning, and provides a rapid clustering method based on density sub-graph estimation, computer equipment and a storage medium in order to overcome the defects that in the prior art, the mass center of a cluster cannot be determined, the calculation cost is high, and over-segmentation occurs in the clustering process. The rapid clustering method based on density sub-graph estimation comprises the following steps: acquiring samples, and preprocessing the samples to form a data set; performing density value estimation on each sample in thedata set, and constructing a density sub-graph set; finding out the highest density point of each density sub-graph from the density sub-graph set to serve as a representative point of the density sub-graph, and forming a candidate set by samples corresponding to the representative points; calculating an important value of each sample in the candidate set; sorting the candidate sets in a descending order according to the