A novel density peak clustering algorithm based on squared residual error
Milan Deepak Parmar, Di Wang, Ah‐Hwee Tan, Chunyan Miao, Jianhua Jiang, You Zhou · 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) · 2017
The density peak clustering (DPC) algorithm is designed to quickly identify intricate-shaped clusters with high dimensionality by finding high-density peaks in a non-iterative manner and using only one threshold parameter. However, DPC has certain limitations in processing low-density data points because it only takes the global data density distribution into account. As such, DPC may confine in forming low-density data clusters, or in other words, DPC may fail in detecting anomalies and borderline points. In this paper, we analyze the limitations of DPC and propose a novel density peak clustering algorithm to better handle low-density clustering tasks. Specifically, our algorithm provides a better decision graph comparing to DPC for the determination of cluster centroids. Experimental results show that our algorithm outperforms DPC and other clustering algorithms on the benchmarking datasets.