Density peaks clustering based on density backbone and fuzzy neighborhood. (November 2020) - Lotfi, Abdulrahman; Moradi, Parham; Beigy, Hamid
Highlights: A novel dynamic density peaks clustering method called DPC-DBFN is proposed. A fuzzy kernel is proposed to compute the local densities of the data points. A graph-based label propagation strategy is used to identify backbones, border areas and noisy points. DPC-DBFN can effectively assign true labels to border points located in overlapped regions. The results on real-world, images and synthetic data show the effectiveness of the proposed method. Abstract: Density peaks clustering (DPC) is as an efficient clustering algorithm due for using a non-iterative process. However, DPC and most of its improvements suffer from the following shortcomings: (1) highly sensitive to its cutoff distance parameter, (2) ignoring the local structure of data in computing local densities, (3) using a crisp kernel to calculate local densities, and (4) suffering from the cause of chain reaction. To address these issues, in this paper a new method called DPC-DBFN is proposed. The proposed method uses a fuzzy kernel for improving separability of clusters and reducing the impact of outliers. DPC-DBFN uses a density-based kNN graph for labeling backbones. This strategy prevents the chain reaction and effectively assigns true labels to those instances located on the border regions to effectively cluster data with various shapes and densities. The DPC-DBFN is evaluated on some real-world and synthetic datasets. The experimental results show the effectiveness and robustness of the proposed algorithm.