Publications -> Conference Papers

A Novel Density Peak Clustering Algorithm based on Squared Residual Error


Authors: M. Parmar, D. Wang, A.-H. Tan, C. Miao, J. Jiang, and Y. Zhou
Title: A Novel Density Peak Clustering Algorithm based on Squared Residual Error
Abstract: 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.
Keywords: Clustering; Density peak clustering; Squared residual error; Low-density data points
Conference Name: International Conference on Security, Pattern Analysis, and Cybernetics (SPAC'17)
Location: Shenzhen, China
Publisher: IEEE
Year: 2017
Accepted PDF File: A_Novel_Density_Peak_Clustering_Algorithm_based_on_Squared_Residual_Error_accepted.pdf
Permanent Link: https://doi.org/10.1109/SPAC.2017.8304248
Reference: M. Parmar, D. Wang, A.-H. Tan, C. Miao, J. Jiang, and Y. Zhou, “A novel density peak clustering algorithm based on squared residual error,” in Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics (SPAC’17). IEEE, December 2017, pp. 43–48.
bibtex: 
@inproceedings{LILY-c139, 
    author	= {Parmar, Milan and Wang, Di and Tan, Ah-Hwee and Miao, Chunyan and Jiang, Jianhua and Zhou, You},
    title	= {A Novel Density Peak Clustering Algorithm based on Squared Residual Error},  
    booktitle	= {Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics (SPAC'17)}, 
    year		= {2017}, 
    month	= {December}, 
    pages	= {43-48}, 
    location	= {Shenzhen, China},
    publisher	= {IEEE},
 }