Clustering by Constructing Hyper-Planes

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...

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Bibliographic Details
Main Authors Diao, Luhong, Gao1, Jinying, Deng, Manman
Format Journal Article
LanguageEnglish
Published 25.04.2020
Subjects
Computer Science - Computer Vision and Pattern Recognition
Online AccessGet full text
DOI10.48550/arxiv.2004.12087

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Summary: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.
DOI:10.48550/arxiv.2004.12087