Continuous greedy randomized adaptive search procedure for data clustering

Cluster analysis is an unsupervised machine learning task that aims at finding the most similar groups of objects, given a prespecified similarity measure. When modeled as an optimization problem, clustering problems generally are NP-hard. Therefore, the use of metaheuristic approaches appears to be a promising alternative. In this paper, a continuous greedy randomized adaptive search procedure (C-GRASP) approach is proposed to solve a partitional clustering problem that aims at minimizing the intra-cluster distances. Computational experiments carried out on existing databases showed that the results obtained by the proposed algorithm was, on average, superior to those found by other well-known metaheuristics, as well as to those achieved by state-of-the-art algorithms from the literature.