A novel bidirectional clustering algorithm based on local density

With the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images.

Automated summary

In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed, which automatically determines the number of clusters, is more sensitive to small clusters, reduces adjusted parameters, and improves clustering performance. The clustering ability of BCALoD is verified through randomly generated datasets and city light satellite images.