An Improved Probability Propagation Algorithm for Density Peak Clustering Based on Natural Nearest Neighborhood
This is an incremental improvement for clustering algorithms, addressing parameter sensitivity to make DPC more robust for complex datasets.
The paper tackled the sensitivity to parameters and limited applicability of the Density Peak Clustering (DPC) algorithm by introducing DPC-PPNNN, which uses natural nearest neighborhood and probability propagation to enable nonparametric clustering, resulting in improved performance over DPC, K-means, and DBSCAN on several datasets.
Clustering by fast search and find of density peaks (DPC) (Since, 2014) has been proven to be a promising clustering approach that efficiently discovers the centers of clusters by finding the density peaks. The accuracy of DPC depends on the cutoff distance ($d_c$), the cluster number ($k$) and the selection of the centers of clusters. Moreover, the final allocation strategy is sensitive and has poor fault tolerance. The shortcomings above make the algorithm sensitive to parameters and only applicable for some specific datasets. To overcome the limitations of DPC, this paper presents an improved probability propagation algorithm for density peak clustering based on the natural nearest neighborhood (DPC-PPNNN). By introducing the idea of natural nearest neighborhood and probability propagation, DPC-PPNNN realizes the nonparametric clustering process and makes the algorithm applicable for more complex datasets. In experiments on several datasets, DPC-PPNNN is shown to outperform DPC, K-means and DBSCAN.