Density peak clustering algorithm based on K neighbors and shared neighbors

The invention discloses a density peak clustering algorithm based on K neighbors and shared neighbors. The density peak clustering algorithm is used for solving the technical problem that an existingdensity peak clustering algorithm is poor in clustering effect. The technical scheme is to improve a DPC algorithm based on the similarity between K-neighbors and shared neighbors, wherein the attribution of each data sample point is determined by KNN distribution information and SNN shared neighbor similarity; and if more points belonging to a certain class cluster in the KNN (i) of the i exist,and the Euclidean distance between the points and the i is closer, the similarity between the two sample points is greater, and the attribution value of the sample i relative to the class cluster to which the KNN (i) belongs is greater while the probability that the sample point i is distributed to the class cluster is greater at the moment.The clustering center appears in an area with higher local density. The density pea