REDPC: A residual error-based density peak clustering algorithm

• Main Authors: PARMAR, Milan, WANG, Di, ZHANG, Xiaofeng, TAN, Ah-hwee, MIAO, Chunyan, ZHOU, You
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Clustering; Density peak clustering; Anomaly detection; Residual error; Low-density data points; Databases and Information Systems; Software Engineering; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5185; https://ink.library.smu.edu.sg/context/sis_research/article/6188/viewcontent/NeuCom2018REDPC.pdf
• Institution: Singapore Management University

The density peak clustering (DPC) algorithm was designed to identify arbitrary-shaped clusters by finding density peaks in the underlying dataset. Due to its aptitudes of relatively low computational complexity and a small number of control parameters in use, DPC soon became widely adopted. However,...