Publication Type
Journal Article
Version
acceptedVersion
Publication Date
8-2020
Abstract
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.
Keywords
Density based clustering, Anchor data points, Local density analysis
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
Neurocomputing
Volume
400
First Page
352
Last Page
370
ISSN
0925-2312
Identifier
10.1016/j.neucom.2020.02.119
Publisher
Elsevier
Citation
WANG, Yizhang; WANG, Di; PANG, Wei; TAN, Ah-hwee; and ZHOU, You.
A systematic density-based clustering method using anchor points. (2020). Neurocomputing. 400, 352-370.
Available at: https://ink.library.smu.edu.sg/sis_research/5183
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.neucom.2020.02.119
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons