@article{377,
  keywords = {Cannot-link, constrained clustering, Differential evolution, Instance-level, Must-link},
  author = {Germán González-Almagro and Julián Luengo and José Ramón Cano De Amo and Salvador García López},
  title = {Enhancing instance-level constrained clustering through differential evolution},
  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-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.},
  year = {2021},
  journal = {Applied Soft Computing},
  volume = {108},
  number = {107435},
  pages = {1-19},
  doi = {10.1016/j.asoc.2021.107435},
  note = {TIN2017-89517-P; PP2019.PRI.I.06.},
}