Multi-clustering algorithm based on improved tensor chain decomposition
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 heter...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2025-06-01
|
| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025043/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849433539159785472 |
|---|---|
| author | ZHANG Hongjun ZHANG Zeyu ZHANG Yingjiao YE Hao PAN Gaojun |
| author_facet | ZHANG Hongjun ZHANG Zeyu ZHANG Yingjiao YE Hao PAN Gaojun |
| author_sort | ZHANG Hongjun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-51a6c0201ed64ba1a82282c35f4e3577 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-51a6c0201ed64ba1a82282c35f4e35772025-08-20T03:27:00ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012025-06-0141103120112428085Multi-clustering algorithm based on improved tensor chain decompositionZHANG HongjunZHANG ZeyuZHANG YingjiaoYE HaoPAN GaojunWith 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025043/tensormulti-clustering algorithmtensor decompositionmulti-source heterogeneous dataprincipal component analysis |
| spellingShingle | ZHANG Hongjun ZHANG Zeyu ZHANG Yingjiao YE Hao PAN Gaojun Multi-clustering algorithm based on improved tensor chain decomposition Dianxin kexue tensor multi-clustering algorithm tensor decomposition multi-source heterogeneous data principal component analysis |
| title | Multi-clustering algorithm based on improved tensor chain decomposition |
| title_full | Multi-clustering algorithm based on improved tensor chain decomposition |
| title_fullStr | Multi-clustering algorithm based on improved tensor chain decomposition |
| title_full_unstemmed | Multi-clustering algorithm based on improved tensor chain decomposition |
| title_short | Multi-clustering algorithm based on improved tensor chain decomposition |
| title_sort | multi clustering algorithm based on improved tensor chain decomposition |
| topic | tensor multi-clustering algorithm tensor decomposition multi-source heterogeneous data principal component analysis |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025043/ |
| work_keys_str_mv | AT zhanghongjun multiclusteringalgorithmbasedonimprovedtensorchaindecomposition AT zhangzeyu multiclusteringalgorithmbasedonimprovedtensorchaindecomposition AT zhangyingjiao multiclusteringalgorithmbasedonimprovedtensorchaindecomposition AT yehao multiclusteringalgorithmbasedonimprovedtensorchaindecomposition AT pangaojun multiclusteringalgorithmbasedonimprovedtensorchaindecomposition |