Divergence-Based Locally Weighted Ensemble Clustering with Dictionary Learning and <i>L</i><sub>2,1</sub>-Norm
Accurate clustering is a challenging task with unlabeled data. Ensemble clustering aims to combine sets of base clusterings to obtain a better and more stable clustering and has shown its ability to improve clustering accuracy. Dense representation ensemble clustering (DREC) and entropy-based locall...
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MDPI AG
2022-09-01
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| Online Access: | https://www.mdpi.com/1099-4300/24/10/1324 |
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| author | Jiaxuan Xu Jiang Wu Taiyong Li Yang Nan |
| author_facet | Jiaxuan Xu Jiang Wu Taiyong Li Yang Nan |
| author_sort | Jiaxuan Xu |
| collection | DOAJ |
| description | Accurate clustering is a challenging task with unlabeled data. Ensemble clustering aims to combine sets of base clusterings to obtain a better and more stable clustering and has shown its ability to improve clustering accuracy. Dense representation ensemble clustering (DREC) and entropy-based locally weighted ensemble clustering (ELWEC) are two typical methods for ensemble clustering. However, DREC treats each microcluster equally and hence, ignores the differences between each microcluster, while ELWEC conducts clustering on clusters rather than microclusters and ignores the sample–cluster relationship. To address these issues, a divergence-based locally weighted ensemble clustering with dictionary learning (DLWECDL) is proposed in this paper. Specifically, the DLWECDL consists of four phases. First, the clusters from the base clustering are used to generate microclusters. Second, a Kullback–Leibler divergence-based ensemble-driven cluster index is used to measure the weight of each microcluster. With these weights, an ensemble clustering algorithm with dictionary learning and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mrow><mn>2</mn><mo>,</mo><mn>1</mn></mrow></msub></semantics></math></inline-formula>-norm is employed in the third phase. Meanwhile, the objective function is resolved by optimizing four subproblems and a similarity matrix is learned. Finally, a normalized cut (Ncut) is used to partition the similarity matrix and the ensemble clustering results are obtained. In this study, the proposed DLWECDL was validated on 20 widely used datasets and compared to some other state-of-the-art ensemble clustering methods. The experimental results demonstrated that the proposed DLWECDL is a very promising method for ensemble clustering. |
| first_indexed | 2024-03-09T20:15:21Z |
| format | Article |
| id | doaj.art-4438d050224a4790947b5bcc9b529cde |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T20:15:21Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-4438d050224a4790947b5bcc9b529cde2023-11-24T00:01:43ZengMDPI AGEntropy1099-43002022-09-012410132410.3390/e24101324Divergence-Based Locally Weighted Ensemble Clustering with Dictionary Learning and <i>L</i><sub>2,1</sub>-NormJiaxuan Xu0Jiang Wu1Taiyong Li2Yang Nan3School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, ChinaSchool of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, ChinaSchool of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, ChinaDepartment of Computer Science, Harbin Finance University, Harbin 150030, ChinaAccurate clustering is a challenging task with unlabeled data. Ensemble clustering aims to combine sets of base clusterings to obtain a better and more stable clustering and has shown its ability to improve clustering accuracy. Dense representation ensemble clustering (DREC) and entropy-based locally weighted ensemble clustering (ELWEC) are two typical methods for ensemble clustering. However, DREC treats each microcluster equally and hence, ignores the differences between each microcluster, while ELWEC conducts clustering on clusters rather than microclusters and ignores the sample–cluster relationship. To address these issues, a divergence-based locally weighted ensemble clustering with dictionary learning (DLWECDL) is proposed in this paper. Specifically, the DLWECDL consists of four phases. First, the clusters from the base clustering are used to generate microclusters. Second, a Kullback–Leibler divergence-based ensemble-driven cluster index is used to measure the weight of each microcluster. With these weights, an ensemble clustering algorithm with dictionary learning and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mrow><mn>2</mn><mo>,</mo><mn>1</mn></mrow></msub></semantics></math></inline-formula>-norm is employed in the third phase. Meanwhile, the objective function is resolved by optimizing four subproblems and a similarity matrix is learned. Finally, a normalized cut (Ncut) is used to partition the similarity matrix and the ensemble clustering results are obtained. In this study, the proposed DLWECDL was validated on 20 widely used datasets and compared to some other state-of-the-art ensemble clustering methods. The experimental results demonstrated that the proposed DLWECDL is a very promising method for ensemble clustering.https://www.mdpi.com/1099-4300/24/10/1324clusteringensemble clustering<i>L</i><sub>2,1</sub>-normsimilaritysubspace clusteringdictionary learning |
| spellingShingle | Jiaxuan Xu Jiang Wu Taiyong Li Yang Nan Divergence-Based Locally Weighted Ensemble Clustering with Dictionary Learning and <i>L</i><sub>2,1</sub>-Norm Entropy clustering ensemble clustering <i>L</i><sub>2,1</sub>-norm similarity subspace clustering dictionary learning |
| title | Divergence-Based Locally Weighted Ensemble Clustering with Dictionary Learning and <i>L</i><sub>2,1</sub>-Norm |
| title_full | Divergence-Based Locally Weighted Ensemble Clustering with Dictionary Learning and <i>L</i><sub>2,1</sub>-Norm |
| title_fullStr | Divergence-Based Locally Weighted Ensemble Clustering with Dictionary Learning and <i>L</i><sub>2,1</sub>-Norm |
| title_full_unstemmed | Divergence-Based Locally Weighted Ensemble Clustering with Dictionary Learning and <i>L</i><sub>2,1</sub>-Norm |
| title_short | Divergence-Based Locally Weighted Ensemble Clustering with Dictionary Learning and <i>L</i><sub>2,1</sub>-Norm |
| title_sort | divergence based locally weighted ensemble clustering with dictionary learning and i l i sub 2 1 sub norm |
| topic | clustering ensemble clustering <i>L</i><sub>2,1</sub>-norm similarity subspace clustering dictionary learning |
| url | https://www.mdpi.com/1099-4300/24/10/1324 |
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