A systematic density-based clustering method using anchor points

NEUROCOMPUTING

卷:400

Article

Wang, Yizhang^1,2  Wang, Di^3,4  Pang, Wei⁵  Miao, Chunyan^3,4,6  Tan, Ah-Hwee^3,6  Zhou, You^1,2 

[1] Jilin Univ, Coll Comp Sci & Technol, Changchun, Peoples R China

[2] Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun, Jilin, Peoples R China

[3] Nanyang Technol Univ, Joint NTU UBC Res Ctr Excellence Act Living Elder, Singapore, Singapore

[4] Nanyang Technol Univ, Joint NTU WeBank Res Ctr FinTech, Singapore, Singapore

[5] Heriot Watt Univ, Sch Math & Comp Sci, Edinburgh, Midlothian, Scotland

[6] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore

关键词: Density based clustering;    Anchor data points;    Local density analysis;   

DOI  :  10.1016/j.neucom.2020.02.119

来源: Elsevier

【 摘 要 】

Clustering is an important unsupervised learning method in machine learning and data mining. Many existing clustering methods may still face the challenge in self-identifying clusters with varying shapes, sizes and densities. To devise a more generic clustering method that considers all the aforementioned properties of the natural clusters, we propose a novel clustering algorithm named Anchor Points based Clustering (APC). The anchor points in APC are characterized by having a relatively large distance from data points with higher densities. We take anchor points as centers to obtain intermediate clusters, which can divide the whole dataset more appropriately so as to better facilitate further grouping. In essence, based on the analysis of the identified anchor points, the relationship among the corresponding intermediate clusters can be better revealed. In short, the difference in local densities (densities within neighboring data points) of the anchor points characterizes their different properties, that is to say, all the intermediate clusters may fall into one or multiple identified levels with different densities. Finally, based on the properties of anchor points, APC spontaneously chooses the appropriate clustering strategies and reports the final clustering results. To evaluate the performances of APC, we conduct experiments on twelve two-dimensional synthetic datasets and twelve multi-dimensional real-world datasets. Moreover, we also apply APC to the Olivetti Face dataset to further assess its effectiveness in terms of face recognition. All experimental results indicate that APC outperforms four classical methods and two state-of-the-art methods in most cases. (C) 2020 Elsevier B.V. All rights reserved.

【 授权许可】

Free