Memetic Differential Evolution Methods for Semi-Supervised Clustering

• URL: http://arxiv.org/abs/2403.04322v2
• Date: Sat, 16 Nov 2024 09:03:05 GMT
• Title: Memetic Differential Evolution Methods for Semi-Supervised Clustering
• Authors: Pierluigi Mansueto, Fabio Schoen,
• Abstract summary: We propose an extension for semi-supervised Minimum Sum-of-Squares Clustering (MSSC) problems of MDEClust. Our new framework, called S-MDEClust, represents the first memetic methodology designed to generate an optimal feasible solution.
• Score: 0.8681835475119588
• License:
• Abstract: 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.

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