基于三阶张量的大规模数据谱聚类集成算法

仵匀政; 杜韬; 周劲; 陈迪; 王心耕

doi:10.11959/j.issn.2096-0271.2024007

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基于三阶张量的大规模数据谱聚类集成算法

研究 | 更新时间：2024-06-03

- 基于三阶张量的大规模数据谱聚类集成算法
- Spectral clustering ensemble algorithm based on three-order tensor for large-scale data
- 大数据 2024年10卷第3期页码：133-148
- 作者机构：
  
  1. 济南大学信息科学与工程学院，山东济南 250024
  2. 山东省网络环境智能计算技术重点实验室，山东济南 250024
- 作者简介：
  
  [ "仵匀政（1998- ），男，济南大学信息科学与工程学院硕士生，主要研究方向为数据挖掘、数据聚类。" ]
  [ "杜韬（1979- ），男，博士，济南大学信息科学与工程学院副教授，主要研究方向为数据挖掘、数据聚类。" ]
  [ "周劲（1976- ），男，博士, 济南大学信息科学与工程学院教授, 山东省人工智能学会理事，主要研究方向为数据挖掘、数据聚类。" ]
  [ "陈迪（1998- ），男，济南大学信息科学与工程学院硕士生，主要研究方向为数据挖掘、数据聚类。" ]
  [ "王心耕（1999- ），男，济南大学信息科学与工程学院硕士生，主要研究方向为数据挖掘、数据聚类。" ]
- 基金信息：
  
  国家自然科学基金项目;The National Natural Science Foundation of China(62273164);国家自然科学基金项目;The National Natural Science Foundation of China(61873324);山东省自然科学基金项目;The Natural Science Foundation of Shandong Province(ZR2019MF040)
- DOI：10.11959/j.issn.2096-0271.2024007
  中图分类号： TP301
- 网络首发：2024-05，
  
  纸质出版：2024-05-15
- 稿件说明：
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仵匀政, 杜韬, 周劲, 等. 基于三阶张量的大规模数据谱聚类集成算法[J]. 大数据, 2024,10(3):133-148.

Yunzheng WU, Tao DU, Jin ZHOU, et al. Spectral clustering ensemble algorithm based on three-order tensor for large-scale data[J]. Big data research, 2024, 10(3): 133-148.
仵匀政, 杜韬, 周劲, 等. 基于三阶张量的大规模数据谱聚类集成算法[J]. 大数据, 2024,10(3):133-148. DOI： 10.11959/j.issn.2096-0271.2024007.

Yunzheng WU, Tao DU, Jin ZHOU, et al. Spectral clustering ensemble algorithm based on three-order tensor for large-scale data[J]. Big data research, 2024, 10(3): 133-148. DOI： 10.11959/j.issn.2096-0271.2024007.

摘要

为了降低大规模数据谱聚类计算负担，进一步提高聚类的准确性和鲁棒性，提出了一种基于三阶张量的大规模数据谱聚类集成算法。首先，提出一种混合代表最近邻近似方法构造数据间的稀疏亲和子矩阵；然后将稀疏亲和子矩阵表示为二部图，通过图分割的方法得到初步聚类结果；最后，提出三阶张量集成方法，将多个聚类结果进行融合，得到最终的聚类结果。在大规模的真实数据集和合成数据集上验证，相较经典的谱聚类算法、聚类集成算法以及近年来对其改进的算法，该算法表现出更优异的性能。

Abstract

In order to reduce the computational burden of large-scale data spectral clustering and further improve the clustering accuracy and robustness

the spectral clustering ensemble algorithm based on the three-order tensor for large-scale data was proposed.The sparse affinity sub-matrix was first constructed by the mixed representative nearest neighbor approximation method.The sparse affinity sub-matrix was then represented as a bipartite graph.The preliminary clustering results were obtained by Graph Segmentation.Finally

an unified clustering result was obtained by fusing multiple clustering results through the three-order tensor ensemble method.On the real datasets and the synthetic datasets

the proposed algorithm showed a better performance compared to the classical spectral clustering algorithm

the clustering ensemble algorithm

and the improved algorithms in recent years.

关键词

Keywords

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