App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 8

Multi-view clustering with diversity constraints and high-order information mining

Zhao Zhenting
Zhao Xujun
School of Computer Science & Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China
DOI: 10.19734/j.issn.1001-3695.2023.12.0615

Abstract

In the current research on multi-view clustering, the majority of methods have not adequately considered the diversity of multiple views nor focused on the high-order neighborhood information of the data. This has led to clustering results that lack accuracy and struggle to uncover the underlying information in datasets. To address these issues, we propose a multi-view clustering method based on diversity constraints and high-order information mining (MVCDCHO) . Firstly, we design a method for measuring diversity between views, utilizing diversity constraints to preserve the intersection features of the data while eliminating differing features across multiple views. Subsequently, we introduce a method for mining high-order information in views, requiring the intersection features of multiple views to approximate a mixed similarity graph, thereby extracting high-order information in data correlations that has been overlooked. Finally, we fuse the intersection features of multiple views into a consensus graph and employ spectral clustering to obtain the clustering target graph. Additionally, we design an alternating iterative method, iteratively learning to optimize the objective function. The experimental results show that the MVCDCHO algorithm has excellent performance on the normalized mutual information (NMI) , the adjusted Rand index (ARI) , and the clustering accuracy (ACC) . Theoretical analysis and experimental studies underscore the crucial role of multi-view diversity and high-order information in the MVCDCHO algorithm, providing evidence for its superiority.

Foundation Support

国家自然科学基金资助项目(61572343)
山西省基础研究计划资助项目(202303021221142)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.12.0615
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 8

Publish History

[2024-03-07] Accepted Paper

Cite This Article

赵振廷, 赵旭俊. 多样性约束和高阶信息挖掘的多视图聚类 [J]. 计算机应用研究, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.12.0615. (Zhao Zhenting, Zhao Xujun. Multi-view clustering with diversity constraints and high-order information mining [J]. Application Research of Computers, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.12.0615. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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