Neighborhood density correlation clustering
Zhenggang Wang, Liu Zhong · 2020
In this paper, by analyzing the advantages and disadvantages of existing clustering analysis algorithms, a new neighborhood density correlation clustering (NDCC) algorithm for quickly discovering arbitrary shaped clusters is proposed that avoids the clustering errors caused by iso-density points between clusters. Because the density of the center region of any cluster sample dataset is greater than that of the edge region, the data points can be divided into core, edge, and noise data points, and then the density correlation of the core data points in their neighborhood can be used to form a cluster. The clustering results of a sample dataset can be formed according to the connection between the core data points and edge points. By constructing an objective function and optimizing the parameters automatically, a locally optimal result that is close to the globally optimal solution can be obtained. This algorithm solves the difficulty of parameter optimization and aspheric clustering in unsupervised clustering to some extent and has excellent potential for application in data analysis and mining.