Density peak clustering algorithm based on density adaptive distance

Abstract

The invention discloses a density peak clustering algorithm based on the density adaptive distance, and aims at solving the problem that a density peak clustering algorithm based on the Euclidean distance is incapable of processing a data set of complex structure effectively. The density peak clustering algorithm based on the density adaptive distance is realized by that (1) the density adaptive distance is calculated according to the Euclidean distance and the adaptive similarity, so that a data space distribution structure is described in a better way; (2) an input parameter, namely the cutoff distance, of the algorithm is calculated according to the proportion of the total number of neighbor points of data points to the total number of a data set sample on the basis of the density adaptive distance; (3) according to the cutoff distance and the density adaptive distance, the local density of each data point as well as the shortest distance from the data point to a point of higher local density are calculated, a decision diagram is drafted, and a clustering center is selected; and (4) each residual point is distributed to a cluster to which the nearest neighbor point of the higher local density belongs, and a clustering result is obtained. Experiments on artificial data sets and UCI real data sets show that the density peak clustering algorithm based on the density adaptive distance, compared with the density peak clustering algorithm based on the Euclidean distance, can handle the data set of complex structure and is higher in accuracy.

Description

technical field [0001] The invention belongs to the technical field of cluster analysis, and mainly relates to the improvement and optimization of a density peak clustering algorithm. Specifically, it is a clustering algorithm based on density-adaptive distance, which can be applied in the fields of pattern recognition, data mining and image processing. Background technique [0002] As an important unsupervised data analysis method, clustering can be used not only as an independent tool to discover hidden information in data, but also as a preprocessing step of other data analysis algorithms. , image processing and other fields have been widely researched and applied. [0003] Clustering is to divide the unknown classification data set into different classes or clusters according to the similarity of data objects, so that the data objects in the same cluster have the maximum similarity, and the data objects between different clusters have the minimum similarity. At present...

AI Technical Summary

Problems solved by technology

However, for those data sets with complex structures, due to the large density difference between different clusters, or there are multiple high-density areas in the same cluster, or the density distribution of the same cluster is relatively uniform, the DPC algorithm is prone to the following two problems: (1) The cluster center is sometimes easily selected by mistake, or even cannot be selected; (2) Even if the cluster center is successfully selected, the ideal clustering result cannot be obtained

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