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
Published inarXiv.org
Main Authors Diao, Luhong, Jinying Gao1, Deng, Manman
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 25.04.2020
Subjects
Algorithms
Clustering
Construction planning
Data points
Datasets
Machine learning
Planes
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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.
ISSN:2331-8422