Density peaks clustering based on k‐nearest neighbors sharing
Summary The density peaks clustering (DPC) algorithm is a density‐based clustering algorithm. Its density peak depends on the density‐distance model to determine it. The definition of local density for samples used in DPC algorithm only considers distance between samples, while the environments of s...
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Published in | Concurrency and computation Vol. 33; no. 5 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Hoboken, USA
John Wiley & Sons, Inc
10.03.2021
Wiley Subscription Services, Inc |
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Abstract | Summary
The density peaks clustering (DPC) algorithm is a density‐based clustering algorithm. Its density peak depends on the density‐distance model to determine it. The definition of local density for samples used in DPC algorithm only considers distance between samples, while the environments of samples are neglected. This leads to the result that DPC algorithm performs poorly on complex data sets with large difference in density, flow pattern or cross‐winding. In the meantime, the fault tolerance of allocation strategy for samples is relatively poor. Based on the findings, this article proposes a density peaks clustering based on k‐nearest neighbors sharing (DPC‐KNNS) algorithm, which uses the similarity between shared neighbors and natural neighbors to define the local density of samples and the allocation. Comparison between theoretical analysis and experiments on various synthetic and real data reveal that the algorithm proposed in this article can discover the cluster center of complex data sets with large difference in density, flow pattern or cross‐winding. It can also provide effective clustering. |
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AbstractList | The density peaks clustering (DPC) algorithm is a density‐based clustering algorithm. Its density peak depends on the density‐distance model to determine it. The definition of local density for samples used in DPC algorithm only considers distance between samples, while the environments of samples are neglected. This leads to the result that DPC algorithm performs poorly on complex data sets with large difference in density, flow pattern or cross‐winding. In the meantime, the fault tolerance of allocation strategy for samples is relatively poor. Based on the findings, this article proposes a density peaks clustering based on k‐nearest neighbors sharing (DPC‐KNNS) algorithm, which uses the similarity between shared neighbors and natural neighbors to define the local density of samples and the allocation. Comparison between theoretical analysis and experiments on various synthetic and real data reveal that the algorithm proposed in this article can discover the cluster center of complex data sets with large difference in density, flow pattern or cross‐winding. It can also provide effective clustering. Summary The density peaks clustering (DPC) algorithm is a density‐based clustering algorithm. Its density peak depends on the density‐distance model to determine it. The definition of local density for samples used in DPC algorithm only considers distance between samples, while the environments of samples are neglected. This leads to the result that DPC algorithm performs poorly on complex data sets with large difference in density, flow pattern or cross‐winding. In the meantime, the fault tolerance of allocation strategy for samples is relatively poor. Based on the findings, this article proposes a density peaks clustering based on k‐nearest neighbors sharing (DPC‐KNNS) algorithm, which uses the similarity between shared neighbors and natural neighbors to define the local density of samples and the allocation. Comparison between theoretical analysis and experiments on various synthetic and real data reveal that the algorithm proposed in this article can discover the cluster center of complex data sets with large difference in density, flow pattern or cross‐winding. It can also provide effective clustering. |
Author | Lv, Li Fan, Tanghuai Han, Longzhe Liu, Baohong Yao, Zhanfeng |
Author_xml | – sequence: 1 givenname: Tanghuai orcidid: 0000-0003-3432-2422 surname: Fan fullname: Fan, Tanghuai organization: School of Information Engineering, Nanchang Institute of Technology – sequence: 2 givenname: Zhanfeng orcidid: 0000-0001-7792-4162 surname: Yao fullname: Yao, Zhanfeng organization: School of Information Engineering, Nanchang Institute of Technology – sequence: 3 givenname: Longzhe orcidid: 0000-0003-2063-0598 surname: Han fullname: Han, Longzhe organization: School of Information Engineering, Nanchang Institute of Technology – sequence: 4 givenname: Baohong orcidid: 0000-0002-8733-7188 surname: Liu fullname: Liu, Baohong organization: School of Information Engineering, Nanchang Institute of Technology – sequence: 5 givenname: Li orcidid: 0000-0002-9705-806X surname: Lv fullname: Lv, Li email: lvli623@163.com organization: School of Information Engineering, Nanchang Institute of Technology |
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Cites_doi | 10.1016/j.neucom.2016.01.009 10.1109/IJCNN.2009.5178917 10.1109/ACCESS.2019.2904254 10.1002/cpe.5567 10.1504/IJBIC.2020.105899 10.1016/j.patcog.2005.09.012 10.1109/2.781637 10.1016/j.ins.2016.03.011 10.1109/COMST.2019.2944748 10.1109/TSMC.1985.6313426 10.1145/1217299.1217303 10.1109/TNNLS.2018.2853710 10.1126/science.1242072 10.1109/ACCESS.2018.2874038 10.1186/1471-2105-8-3 10.1109/ICICICT1.2017.8342664 10.1016/j.knosys.2017.07.010 10.1504/IJBIC.2016.078666 10.1360/N112015-00135 10.1016/j.neucom.2015.05.109 10.1109/ICPR.2016.7899678 10.1016/j.patcog.2017.09.045 10.1109/TPAMI.2002.1033218 10.1109/LGRS.2017.2786732 10.1109/TII.2018.2822680 10.1016/S0031-3203(02)00060-2 10.1007/s13042-017-0636-1 10.1109/TKDE.2002.1033770 10.1007/s12293-017-0237-2 10.1016/j.patcog.2007.04.010 10.1007/s11265-019-01459-4 10.1080/01621459.1983.10478008 10.1016/j.future.2016.10.019 10.1007/11590316_1 |
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Notes | Funding information National Natural Science Foundation of China, 61663029 and 62066030; Natural Science Foundation of Jiangxi Province, 20192BAB207031; The Science Fund for Distinguished Young Scholars of Jiangxi Province, 2018ACB21029 |
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The density peaks clustering (DPC) algorithm is a density‐based clustering algorithm. Its density peak depends on the density‐distance model to... The density peaks clustering (DPC) algorithm is a density‐based clustering algorithm. Its density peak depends on the density‐distance model to determine it.... |
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SubjectTerms | Algorithms Clustering Datasets Density density peak clustering Fault tolerance k‐nearest neighbors local density natural neighbors shared neighbors Winding |
Title | Density peaks clustering based on k‐nearest neighbors sharing |
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