Spectral Clustering Algorithm Based on OptiSim Selection

被引：0
|
作者：
Liu, Xuejuan [1 ]
Wang, Junguo [2 ]
Yuan, Xiangying [3 ]
机构：
[1] Lecturer of School of Accounting, Nanjing University of Finance & Economics, Nanjing,210023, China
[2] Information Management Center, Nanjing Forest Police College, Nanjing,210023, China
[3] Information Management Center, Nanjing Forest Police College, Nanjing,210023, China
来源：
IAENG International Journal of Applied Mathematics | 2021年 / 51卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Arbitrary structures - Clustering effect - Clustering results - SC algorithms - Selection algorithm - Similarity matrix - Spectral clustering - Spectral clustering algorithms;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The spectral clustering (SC) method has a good clustering effect on arbitrary structure datasets because of its solid theoretical basis. However, the required time complexity is high, thus limiting the application of SC in big datasets. To reduce time complexity, we propose an SC algorithm based on OptiSim Selection (SCOSS) in this study. This new algorithm starts from selecting a representative subset by using an optimizable k-dissimilarity selection algorithm (OptiSim) and then uses the Nyström method to approximate the eigenvectors of the similarity matrix. Theoretical deductions and experiment results show that the proposed algorithm can use less clustering time to achieve a good clustering result. © 2021. All Rights Reserved.
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