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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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 455; pp. 401 - 418
Main Authors: Guan, Junyi, Li, Sheng, He, Xiongxiong, Zhu, Jinhui, Chen, Jiajia
Format: Journal Article
Language:English
Published: Elsevier B.V 30.09.2021
Subjects:
Clustering
Density peak
Hierarchical clustering
KNN
99-00
Density peak
KNN
00-01
Clustering
Hierarchical clustering
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2021.05.071