Clustering method and device based on density peak value

Abstract

The invention discloses a clustering method and device based on a density peak value, and the method comprises the steps: randomly selecting a plurality of observation points in a sample space; obtaining a clustering result of each observation point according to the distance between each sample point and each observation point; generating a similarity matrix of high-dimensional data according to the clustering results of all observation points; enabling the sum of all rows of the similarity matrix to serve as the density of sample points corresponding to each row of the similarity matrix; obtaining a density set of the similarity matrix according to the density of sample points corresponding to each row of the similarity matrix; calculating the peak value of each density in the density set; determining a candidate center according to the peak value of each density; carrying out the clustering of the peak values of the candidate centers, and obtaining the clustering result of each sample point. According to the invention, there is no need of the setting of cutoff distance parameters and there is no need of a decision making map for the manual selection of clustering centers, so themethod is wider in application range.

Description

technical field [0001] The invention relates to the field of cluster analysis, in particular to a density peak-based clustering method and device. Background technique [0002] Clustering is the process of dividing the data set samples into reasonable clusters according to the similarity between data objects. The clustering results make the objects in the same cluster have high similarity, and the objects between different clusters have low similarity. Widely used in scientific data analysis and engineering systems and other fields. [0003] Clustering algorithms include partitioning clustering methods, hierarchical clustering methods, density-based clustering methods, grid-based clustering methods and integrated clustering algorithms. The Kmeans clustering algorithm is the most widely used partitioning clustering algorithm. However, the clustering results of the kmeans clustering algorithm are heavily dependent on the initial cluster centers, and non-convex clusters cannot...

AI Technical Summary

Problems solved by technology

[0005] The main purpose of the present invention is to provide a clustering method and device based on density peaks, aiming to solve the problem that the DPC algorithm in the prior art need...

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