D-NND: A Hierarchical Density Clustering Method via Nearest Neighbor Descent
Teng Qiu, Chaoyi Li, Yongjie Li · 2018
Most density-based clustering methods largely rely on how well the underlying density is estimated. However, like clustering, density estimation is also a challenging unsupervised learning problem, especially the determination of the kernel bandwidth. In this paper, we propose a density-based multilayer hierarchical clustering method, called the Deep Nearest Neighbor Descent (D-NND), which can largely alleviate the impact of the density estimation. Unlike previous density-based methods, D-NND learns the underlying density distribution layer by layer and at the same time makes the dataset sparsely and effectively organized into a directed Tree. The experiments on three real-world datasets and several challenging synthetic datasets demonstrate that the proposed method has strong ability to discover the underlying cluster structures and is not very sensitive to the density estimation method, the parameters and the clusters of multiple scales.