@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}
}
