Multi-clustering algorithm based on improved tensor chain decomposition

ZHANG Hongjun; ZHANG Zeyu; ZHANG Yingjiao; YE Hao; PAN Gaojun

doi:10.11959/j.issn.1000-0801.2025043

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Multi-clustering algorithm based on improved tensor chain decomposition

Research and Development | 更新时间：2025-06-28

- Multi-clustering algorithm based on improved tensor chain decomposition
- Telecommunications Science Vol. 41, Issue 6, Pages: 103-120(2025)
- 作者机构：
  
  1.中国通信服务股份有限公司，北京 100073
  2.南京邮电大学物联网学院，江苏南京 210003
  3.曼彻斯特大学人工智能学院，英国曼彻斯特 M13 9PL
  4.南京邮电大学现代邮政学院，江苏南京 210003
  5.华为技术有限公司南京研究所，江苏南京 210012
  6.北京外企德科人力资源服务上海有限公司，上海 200011
  7.浙江省通信产业服务有限公司，浙江杭州 310052
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61972208;62102194;62272239);Jiangsu Graduate Research and Practice Innovation Program Project (No.KYCX22_1027(SJCX24_0339);SJCX24_0346), Nanjing University of Posts and Telecommunications
- DOI：10.11959/j.issn.1000-0801.2025043
  CLC： TP393
- Received：23 November 2024，
  
  Revised：2025-02-25，
  
  Published：20 June 2025
- 稿件说明：
移动端阅览
张宏俊,张泽宇,张颖娇等.基于改进张量链分解的多聚类算法[J].电信科学,2025,41(06):103-120.

ZHANG Hongjun,ZHANG Zeyu,ZHANG Yingjiao,et al.Multi-clustering algorithm based on improved tensor chain decomposition[J].Telecommunications Science,2025,41(06):103-120.
张宏俊,张泽宇,张颖娇等.基于改进张量链分解的多聚类算法[J].电信科学,2025,41(06):103-120. DOI： 10.11959/j.issn.1000-0801.2025043.

ZHANG Hongjun,ZHANG Zeyu,ZHANG Yingjiao,et al.Multi-clustering algorithm based on improved tensor chain decomposition[J].Telecommunications Science,2025,41(06):103-120. DOI： 10.11959/j.issn.1000-0801.2025043.

摘要

随着大数据时代的到来，高阶数据的有效表示和分析成为一项重大挑战。基于此，聚焦于张量分解技术在多聚类算法中的应用，特别是针对大型多源异构数据集的处理，深入研究并改进了张量链（tensor train，TT）分解方法，通过引入新的优化策略，显著提高了其在多聚类任务中的性能。创新主要体现在两个方面：一是提出了一种新的张量分解框架，该框架通过优化目标函数，有效降低了存储成本并提高了计算效率；二是将改进的张量分解技术应用于3种主要的多聚类算法中，包括自加权多视图聚类（self-weighted multi-view clustering，SwMC）、潜在多视图子空间聚类（latent multi-view subspace clustering，LMSC）和具有完整性感知相似性的多视图子空间聚类（multi-view subspace clustering with intactness-aware similarity，MSC IAS），显著提升了聚类的准确性和效率。为了验证方法的有效性，在7个真实的数据集上进行了全面的实验评估，包括准确性（accuracy，ACC）、归一化互信息（normalized mutual information，NMI）和纯度等3个指标。实验结果表明，所提出的方法在提取有意义的模式和提高聚类性能方面具有显著优势。

Abstract

With the advent of the era of big data

the effective representation and analysis of high-level data has become a major challenge. Based on this

the application of tensor decomposition technology in multi-clustering algorithms was focused on especially for the processing of large multi-source heterogeneous datasets. The tensor train (TT) method was studied and improved in depth

which had significantly improved its performance in multi-clustering tasks by introducing a new optimization strategy. The innovations were mainly reflected in two aspects: firstly

a new tensor decomposition framework was proposed

which effectively reduced the storage cost and improved the computational efficiency by optimizing the objective function; secondly

the improved tensor decomposition technique was applied to three main multi-clustering algorithms

including self-weighted multi-view clustering (SwMC)

latent multi-view subspace clustering (LMSC)

and multi-view subspace clustering with intactness-aware similarity (MSC IAS)

which significantly improved the accuracy and efficiency of clustering. To validate the effectiveness of the proposed methodology

comprehensive experiments were conducted on seven real-world datasets

including assessments of key metrics such as accuracy (ACC)

normalized mutual information (NMI)

and purity. Experimental results show that the proposed method has significant advantages in extracting meaningful patterns and improving clustering performance.

关键词

Keywords

references

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