# Memetic Differential Evolution Methods for Semi-Supervised Clustering

arXiv: https://arxiv.org/abs/2403.04322 · HTML: https://mlsanity.com/paper/2403.04322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 50 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 4 of 4)

- [An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering](https://mlsanity.com/paper/2111.15571/) (2021)
- [HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering](https://mlsanity.com/paper/1804.09813/) (2018)
- [An algorithm for clustering with confidence-based must-link and cannot-link constraints](https://mlsanity.com/paper/2212.14437/) (2022)
- [Semi-Supervised Clustering with Inaccurate Pairwise Annotations](https://mlsanity.com/paper/2104.02146/) (2021)

## Abstract (arXiv)

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.