A novel density peak clustering algorithm based on squared residual error

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 be...

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Main Authors:	Parmar, Milan, Wang, Di, Tan, Ah-Hwee, Miao, Chunyan, Jiang, Jianhua
Other Authors:	School of Computer Science and Engineering
Format:	Conference or Workshop Item
Language:	English
Published:	2018
Subjects:	Clustering Density Peak Clustering DRNTU::Engineering::Computer science and engineering
Online Access:	https://hdl.handle.net/10356/89594 http://hdl.handle.net/10220/47062
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-895942020-03-07T11:48:46Z A novel density peak clustering algorithm based on squared residual error Parmar, Milan Wang, Di Tan, Ah-Hwee Miao, Chunyan Jiang, Jianhua School of Computer Science and Engineering 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) NTU-UBC Research Centre of Excellence in Active Living for the Elderly Clustering Density Peak Clustering DRNTU::Engineering::Computer science and engineering 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. NRF (Natl Research Foundation, S’pore) Accepted version 2018-12-18T07:25:48Z 2019-12-06T17:29:10Z 2018-12-18T07:25:48Z 2019-12-06T17:29:10Z 2017-12-01 2017 Conference Paper 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 https://hdl.handle.net/10356/89594 http://hdl.handle.net/10220/47062 10.1109/SPAC.2017.8304248 208265 en © 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]. 6 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	Clustering Density Peak Clustering DRNTU::Engineering::Computer science and engineering
spellingShingle	Clustering Density Peak Clustering DRNTU::Engineering::Computer science and engineering Parmar, Milan Wang, Di Tan, Ah-Hwee Miao, Chunyan Jiang, Jianhua A novel density peak clustering algorithm based on squared residual error
description	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.
author2	School of Computer Science and Engineering
author_facet	School of Computer Science and Engineering Parmar, Milan Wang, Di Tan, Ah-Hwee Miao, Chunyan Jiang, Jianhua
format	Conference or Workshop Item
author	Parmar, Milan Wang, Di Tan, Ah-Hwee Miao, Chunyan Jiang, Jianhua
author_sort	Parmar, Milan
title	A novel density peak clustering algorithm based on squared residual error
title_short	A novel density peak clustering algorithm based on squared residual error
title_full	A novel density peak clustering algorithm based on squared residual error
title_fullStr	A novel density peak clustering algorithm based on squared residual error
title_full_unstemmed	A novel density peak clustering algorithm based on squared residual error
title_sort	novel density peak clustering algorithm based on squared residual error
publishDate	2018
url	https://hdl.handle.net/10356/89594 http://hdl.handle.net/10220/47062
_version_	1681037436671492096

A novel density peak clustering algorithm based on squared residual error

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