Fast hierarchical clustering of local density peaks via an association degree transfer method
Density Peak clustering (DPC) as a novel algorithm can fast identify density peaks. But it comes along with two drawbacks: its allocation strategy may produce some non-adjacent associations that may lead to poor clustering results and even cause the malfunction of its cluster center selection method...
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| Published in: | Neurocomputing (Amsterdam) Vol. 455; pp. 401 - 418 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
30.09.2021
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | Density Peak clustering (DPC) as a novel algorithm can fast identify density peaks. But it comes along with two drawbacks: its allocation strategy may produce some non-adjacent associations that may lead to poor clustering results and even cause the malfunction of its cluster center selection method to mistakenly identify cluster centers; it may perform poorly with its high complex O(n2) when comes to large-scale data. Herein, a fast hierarchical clustering of local density peaks via an association degree transfer method (FHC-LDP) is proposed. To avoid DPC’s drawbacks caused by non-adjacent associations, FHC-LDP only considers the association between neighbors and design an association degree transfer method to evaluate the association between points that are not neighbors. FHC-LDP can fast identify local density peaks as sub-cluster centers to generate sub-clusters automatically and evaluate the similarity between sub-clusters. Then, by analyzing the similarity of sub-cluster centers, a hierarchical structure of sub-clusters is built. FHC-LDP replaces DPC’s cluster center selection method with a bottom-up hierarchical approach to ensure sub-clusters in each cluster are most similar. In FHC-LDP, only neighbor information of data is required, so by using a fast KNN algorithm, FHC-LDP can run about O(nlog(n)). Experimental results demonstrate FHC-LDP is remarkably superior to traditional clustering algorithms and other variants of DPC in recognizing cluster structure and running speed. |
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| AbstractList | Density Peak clustering (DPC) as a novel algorithm can fast identify density peaks. But it comes along with two drawbacks: its allocation strategy may produce some non-adjacent associations that may lead to poor clustering results and even cause the malfunction of its cluster center selection method to mistakenly identify cluster centers; it may perform poorly with its high complex O(n2) when comes to large-scale data. Herein, a fast hierarchical clustering of local density peaks via an association degree transfer method (FHC-LDP) is proposed. To avoid DPC’s drawbacks caused by non-adjacent associations, FHC-LDP only considers the association between neighbors and design an association degree transfer method to evaluate the association between points that are not neighbors. FHC-LDP can fast identify local density peaks as sub-cluster centers to generate sub-clusters automatically and evaluate the similarity between sub-clusters. Then, by analyzing the similarity of sub-cluster centers, a hierarchical structure of sub-clusters is built. FHC-LDP replaces DPC’s cluster center selection method with a bottom-up hierarchical approach to ensure sub-clusters in each cluster are most similar. In FHC-LDP, only neighbor information of data is required, so by using a fast KNN algorithm, FHC-LDP can run about O(nlog(n)). Experimental results demonstrate FHC-LDP is remarkably superior to traditional clustering algorithms and other variants of DPC in recognizing cluster structure and running speed. |
| Author | Guan, Junyi Zhu, Jinhui He, Xiongxiong Li, Sheng Chen, Jiajia |
| Author_xml | – sequence: 1 givenname: Junyi surname: Guan fullname: Guan, Junyi organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China – sequence: 2 givenname: Sheng surname: Li fullname: Li, Sheng email: shengli@zjut.edu.cn organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China – sequence: 3 givenname: Xiongxiong surname: He fullname: He, Xiongxiong email: hxx@zjut.edu.cn organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China – sequence: 4 givenname: Jinhui surname: Zhu fullname: Zhu, Jinhui email: 2512016@zju.edu.cn organization: Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China – sequence: 5 givenname: Jiajia surname: Chen fullname: Chen, Jiajia organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China |
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