Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism

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.

TIN2017-89517-P; PP2016.PRI.I.02.