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A systematic density-based clustering method using anchor points
- Source :
- Neurocomputing. 400:352-370
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 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. AI Singapore Ministry of Health (MOH) National Research Foundation (NRF) Accepted version This research is supported by the National Natural Science Foundation of China (61772227,61572227), the Science & Technology Development Founda- tion of Jilin Province (20180201045GX) and the Social Science Foundation of Education Department of Jilin Province (JJKH20181315SK). This research is also supported, in part, by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: AISG-GC-2019-003), the Singapore Ministry of Health under its National Innovation Challenge on Active and Confident Ageing (NIC Project No. MOH/NIC/COG04/2017), and the Joint NTU-WeBank Research Centre on Fintech, Nanyang Technological University, Singapore.
- Subjects :
- 0209 industrial biotechnology
Computer science
business.industry
Cognitive Neuroscience
Pattern recognition
02 engineering and technology
Facial recognition system
Computer Science Applications
Anchor Data Points
020901 industrial engineering & automation
Artificial Intelligence
Face (geometry)
Still face
Density Based Clustering
0202 electrical engineering, electronic engineering, information engineering
Computer science and engineering [Engineering]
Unsupervised learning
020201 artificial intelligence & image processing
Artificial intelligence
business
Cluster analysis
Density based clustering
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 400
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....10eb770a1c082880ac4a9244866b3214