Clustering by Constructing Hyper-Planes

• URL: http://arxiv.org/abs/2004.12087v1
• Date: Sat, 25 Apr 2020 08:52:21 GMT
• Title: Clustering by Constructing Hyper-Planes
• Authors: Luhong Diao (1,2), Jinying Gao1 (1,2), Manman Deng (1,2) ((1) Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing, China.(2) College of Applied Sciences, Beijing University of Technology, Beijing, China.)
• Abstract summary: We present a clustering algorithm by finding hyper-planes to distinguish data points. It relies on the marginal space between the points to determine centers and numbers of clusters. Because the algorithm is based on linear structures, it can approximate the distribution of datasets accurately and flexibly.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a kind of basic machine learning method, clustering algorithms group data points into different categories based on their similarity or distribution. We present a clustering algorithm by finding hyper-planes to distinguish the data points. It relies on the marginal space between the points. Then we combine these hyper-planes to determine centers and numbers of clusters. Because the algorithm is based on linear structures, it can approximate the distribution of datasets accurately and flexibly. To evaluate its performance, we compared it with some famous clustering algorithms by carrying experiments on different kinds of benchmark datasets. It outperforms other methods clearly.

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