Density peak clustering algorithm based on K neighbors and shared neighbors

Abstract

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 peak clustering algorithm provided by the invention avoids the defect that the DPC algorithm measures the sample density and the associated distribution error similar to the domino effect generated during sample distribution, and the clustering effect is good.

Description

technical field [0001] The invention relates to a density peak clustering algorithm, in particular to a density peak clustering algorithm based on K-nearest neighbors and shared nearest neighbors. Background technique [0002] Cluster analysis is a statistical analysis method for studying classification problems, and it is an important technique for data mining. It classifies data samples and object sets in an unsupervised manner, and is widely used in data mining, pattern recognition, document retrieval, and image segmentation. field. Especially in the context of big data, the existence of massive and diverse data makes the research of clustering algorithms with automatic understanding, processing and summarization of data attract extensive attention. The main purpose of clustering is to divide a given group into groups or clusters with common characteristics, so that the data similarity within a group is relatively high, while the differences between groups are more obvio...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the deficiency of poor clustering effect of the existing density peak clustering algorithm, the present invention provides a density peak clustering algorithm based on K nearest neighbors and shared nearest neighbors

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