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Clustering by Constructing Hyper-Planes
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DOI：10.1109/ACCESS.2021.3078584.png)
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Abstract
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As a ubiquitous method in the field of machine learning, clustering algorithm attracts a lot attention. Because only some basic information can be utilized, clustering data points into correct categories is a critical task especially when the cluster number is unknown. This paper presents an algorithm which can find the cluster number automatically. It firstly constructs hyper-planes based on the marginal of sample points. Then an adjacent relationship between data points is defined. Based on it, connective components are derived. According to a validity index proposed in this paper, the high-qualified connective components are selected as cluster centers. Meanwhile, the clusters' number is also determined. Another contribution of this paper is that all the parameters in this algorithm can be set automatically. To evaluate its robustness, experiments on different kinds of benchmark datasets are carried out. They show that the performances are even better than some other methods' best results which are selected manually.
Keywords:
Clustering algorithms
Manifolds
Indexes
Machine learning algorithms
Support vector machines
Machine learning
Licenses
Clustering algorithm
hyper-planes
support vector machine
validity index
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