Academic Journal
Data envelopment analysis clustering: an optimization based machine learning.
| Τίτλος: | Data envelopment analysis clustering: an optimization based machine learning. |
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| Συγγραφείς: | An, Qingxian, Zhao, Jing, Zhu, Joe |
| Πηγή: | Journal of the Operational Research Society; Jun2026, Vol. 77 Issue 6, p1496-1512, 17p |
| Θεματικοί όροι: | Data envelopment analysis, Clustering algorithms, Mathematical optimization, Machine learning, Medical informatics, Cost analysis |
| Περίληψη: | Clustering algorithms are commonly used to group units based on their intrinsic characteristics, represented by various features, typically divided into cost features and benefit features. Most existing clustering methods analyze these features based on distance functions, and the cost-benefit relationship is not a focus. In this study, we approach clustering from the perspective of constructing a composite index reflecting the cost-benefit ratio. We utilize data envelopment analysis (DEA) to develop a "k-DEA-weights" clustering method to identify clusters and reveal the relative degree of importance of different features within each cluster to enhance the explainability of clustering outcomes. The proposed method can be viewed as an optimization-based explainable machine learning technique. We first employ a DEA cross-efficiency model to calculate cross-evaluated weights for units and a group DEA cross-efficiency model to obtain common weights for clusters. Through an iterative heuristic algorithm, we optimize the clustering process by minimizing the distance between the cross-evaluated weights and the common weights within a cluster. To validate its effectiveness, we conduct numerical experiments that demonstrate strong clustering performance. We then apply it to Chunyu Doctor platform, a Chinese online healthcare consultation platform. The results provide actionable insights for optimizing services and improving user experience. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Data envelopment analysis clustering: an optimization based machine learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22An%2C+Qingxian%22">An, Qingxian</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Jing%22">Zhao, Jing</searchLink><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Joe%22">Zhu, Joe</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of the Operational Research Society; Jun2026, Vol. 77 Issue 6, p1496-1512, 17p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Data+envelopment+analysis%22">Data envelopment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+informatics%22">Medical informatics</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+analysis%22">Cost analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Clustering algorithms are commonly used to group units based on their intrinsic characteristics, represented by various features, typically divided into cost features and benefit features. Most existing clustering methods analyze these features based on distance functions, and the cost-benefit relationship is not a focus. In this study, we approach clustering from the perspective of constructing a composite index reflecting the cost-benefit ratio. We utilize data envelopment analysis (DEA) to develop a "k-DEA-weights" clustering method to identify clusters and reveal the relative degree of importance of different features within each cluster to enhance the explainability of clustering outcomes. The proposed method can be viewed as an optimization-based explainable machine learning technique. We first employ a DEA cross-efficiency model to calculate cross-evaluated weights for units and a group DEA cross-efficiency model to obtain common weights for clusters. Through an iterative heuristic algorithm, we optimize the clustering process by minimizing the distance between the cross-evaluated weights and the common weights within a cluster. To validate its effectiveness, we conduct numerical experiments that demonstrate strong clustering performance. We then apply it to Chunyu Doctor platform, a Chinese online healthcare consultation platform. The results provide actionable insights for optimizing services and improving user experience. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/01605682.2025.2537278 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1496 Subjects: – SubjectFull: Data envelopment analysis Type: general – SubjectFull: Clustering algorithms Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Medical informatics Type: general – SubjectFull: Cost analysis Type: general Titles: – TitleFull: Data envelopment analysis clustering: an optimization based machine learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: An, Qingxian – PersonEntity: Name: NameFull: Zhao, Jing – PersonEntity: Name: NameFull: Zhu, Joe IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01605682 Numbering: – Type: volume Value: 77 – Type: issue Value: 6 Titles: – TitleFull: Journal of the Operational Research Society Type: main |
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