@article {793, title = {Enhancing instance-level constrained clustering through differential evolution}, journal = {Applied Soft Computing}, volume = {108}, number = {107435}, year = {2021}, note = {TIN2017-89517-P; PP2019.PRI.I.06.}, pages = {1-19}, abstract = {Clustering has always been a powerful tool in knowledge discovery. Traditionally unsupervised, it received renewed attention when it was shown to produce better results when provided with new types of information, thus leading to a new kind of semi-supervised learning: constrained clustering. This technique is a generalization of traditional clustering that considers additional information encoded by constraints. Constraints can be given in the form of instance-level must-link and cannot-link constraints, which this paper focuses on. We propose the first application of Differential Evolution to the constrained clustering problem, which has proven to produce a better exploration{\textendash}exploitation trade-off when comparing with previous approaches. We will compare the results obtained by this proposal to those obtained by previous nature-inspired techniques and by some of the state-of-the-art algorithms on 25 datasets with incremental levels of constraint-based information, supporting our conclusions with the aid of Bayesian statistical tests.}, keywords = {Cannot-link, constrained clustering, Differential evolution, Instance-level, Must-link}, doi = {https://doi.org/10.1016/j.asoc.2021.107435}, author = {Germ{\'a}n Gonz{\'a}lez-Almagro and Luengo, Juli{\'a}n and J. R. Cano and Garc{\'\i}a, Salvador} }