@conference {790,
	title = {Agglomerative Constrained Clustering Through Similarity and Distance Recalculation},
	booktitle = {International Conference on Hybrid Artificial Intelligence Systems},
	year = {2020},
	pages = {424-436},
	abstract = {Constrained clustering has become a topic of considerable interest in machine learning, as it has been shown to produce promising results in domains where only partial information about how to solve the problem is available. Constrained clustering can be viewed as a semi-supervised generalization of clustering, which is traditionally unsupervised. It is able to leverage a new type of information encoded by constraints that guide the clustering process. In particular, this study focuses on instance-level must-link and cannot-link constraints. We propose an agglomerative constrained clustering algorithm, which combines distance-based and clustering-engine adapting methods to incorporate constraints into the partitioning process. It computes a similarity measure on the basis of distances (in the dataset) and constraints (in the constraint set) to later apply an agglomerative clustering method, whose clustering engine has been adapted to consider constraints and raw distances. We prove its capability to produce quality results for the constrained clustering problem by comparing its performance to previous proposals on several datasets with incremental levels of constraint-based information.},
	keywords = {Agglomerative clustering, constrained clustering, Semi-supervised learning, Similarity recalculation},
	doi = {https://doi.org/10.1007/978-3-030-61705-9_35},
	author = {Germ{\'a}n Gonz{\'a}lez-Almagro and Juan Luis Suarez and Luengo, Juli{\'a}n and J. R. Cano and Garc{\'\i}a, Salvador}
}
@conference {788,
	title = {Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism},
	booktitle = {GECCO {\textquoteright}20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference},
	year = {2020},
	note = {TIN2017-89517-P; PP2016.PRI.I.02.},
	month = {06/2020},
	pages = {333{\textendash}341},
	abstract = {Clustering has always been a topic of interest in knowledge discovery, it is able to provide us with valuable information within the unsupervised machine learning framework. It received renewed attention when it was shown to produce better results in environments where partial information about how to solve the problem is available, thus leading to a new machine learning paradigm: semi-supervised machine learning. This new type of information can be given in the form of constraints, which guide the clustering process towards quality solutions. In particular, this study considers the pairwise instance-level must-link and cannot-link constraints. Given the ill-posed nature of the constrained clustering problem, we approach it from the multiobjective optimization point of view. Our proposal consists in a memetic elitist evolutionary strategy that favors exploitation by applying a local search procedure to the elite of the population and transferring its results only to the external population, which will also be used to generate new individuals. We show the capability of this method to produce quality results for the constrained clustering problem when considering incremental levels of constraint-based information. For the comparison with state-of-the-art methods, we include previous multi-objective approaches, single-objective genetic algorithms and classic constrained clustering methods.},
	keywords = {constrained clustering, memetic elitis MOEA, multiobjective optimization, pairwise instance- level constraints, Semi-supervised learning},
	doi = {https://doi.org/10.1145/3377930.3390187},
	author = {Germ{\'a}n Gonz{\'a}lez-Almagro and Rosales-P{\'e}rez, Alejandro and Luengo, Juli{\'a}n and J. R. Cano and Garc{\'\i}a, Salvador}
}
