Memetic Differential Evolution Methods for Semi-Supervised Clustering
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Mansueto, Pierluigi Schoen, Fabio |
| author_facet | Mansueto, Pierluigi Schoen, Fabio |
| contents | In this paper, we propose an extension for semi-supervised Minimum Sum-of-Squares Clustering (MSSC) problems of MDEClust, a memetic framework based on the Differential Evolution paradigm for unsupervised clustering. In semi-supervised MSSC, background knowledge is available in the form of (instance-level) "must-link" and "cannot-link" constraints, each of which indicating if two dataset points should be associated to the same or to a different cluster, respectively. The presence of such constraints makes the problem at least as hard as its unsupervised version and, as a consequence, some framework operations need to be carefully designed to handle this additional complexity: for instance, it is no more true that each point is associated to its nearest cluster center. As far as we know, our new framework, called S-MDEClust, represents the first memetic methodology designed to generate a (hopefully) optimal feasible solution for semi-supervised MSSC problems. Results of thorough computational experiments on a set of well-known as well as synthetic datasets show the effectiveness and efficiency of our proposal. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04322 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Memetic Differential Evolution Methods for Semi-Supervised Clustering Mansueto, Pierluigi Schoen, Fabio Optimization and Control Machine Learning Neural and Evolutionary Computing 90C11, 90C30, 90C59 In this paper, we propose an extension for semi-supervised Minimum Sum-of-Squares Clustering (MSSC) problems of MDEClust, a memetic framework based on the Differential Evolution paradigm for unsupervised clustering. In semi-supervised MSSC, background knowledge is available in the form of (instance-level) "must-link" and "cannot-link" constraints, each of which indicating if two dataset points should be associated to the same or to a different cluster, respectively. The presence of such constraints makes the problem at least as hard as its unsupervised version and, as a consequence, some framework operations need to be carefully designed to handle this additional complexity: for instance, it is no more true that each point is associated to its nearest cluster center. As far as we know, our new framework, called S-MDEClust, represents the first memetic methodology designed to generate a (hopefully) optimal feasible solution for semi-supervised MSSC problems. Results of thorough computational experiments on a set of well-known as well as synthetic datasets show the effectiveness and efficiency of our proposal. |
| title | Memetic Differential Evolution Methods for Semi-Supervised Clustering |
| topic | Optimization and Control Machine Learning Neural and Evolutionary Computing 90C11, 90C30, 90C59 |
| url | https://arxiv.org/abs/2403.04322 |