Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89594
Title: A novel density peak clustering algorithm based on squared residual error
Authors: Parmar, Milan
Wang, Di
Tan, Ah-Hwee
Miao, Chunyan
Jiang, Jianhua
Keywords: Clustering
Density Peak Clustering
DRNTU::Engineering::Computer science and engineering
Issue Date: 2017
Source: Parmar, M., Wang, D., Tan, A.-H., Miao, C., & Jiang, J. (2017). A novel density peak clustering algorithm based on squared residual error. 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 43-48. doi:10.1109/SPAC.2017.8304248
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.
URI: https://hdl.handle.net/10356/89594
http://hdl.handle.net/10220/47062
DOI: 10.1109/SPAC.2017.8304248
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/SPAC.2017.8304248].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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