A novel density peak clustering algorithm based on squared residual error

• Main Authors: PARMAR, Milan, WANG, Di, TAN, Ah-hwee, MIAO, Chunyan, JIANG, Jianhua, ZHOU, You
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: clustering; density peak clustering; squared residual error; low-density data points; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5465; https://ink.library.smu.edu.sg/context/sis_research/article/6468/viewcontent/SPAC2017C.pdf
• Institution: Singapore Management University

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.