A comparative study of unsupervised image clustering systems. (9th July 2019) - Bettoumi, Safa; Jlassi, Chiraz; Arous, Naet
The purpose of clustering algorithms is to give sense and extract value from large sets of structured and unstructured data. Thus, clustering is present in all science areas that use automatic learning. Therefore, we present in this paper a comparative study and an evaluation of different clustering methods proposed in the literature such as prototype based clustering, fuzzy and probabilistic clustering, hierarchical clustering and density based clustering. We present also an analysis of advantages and disadvantages of these clustering methods based essentially on experimentation. Extensive experiments are conducted on three real-world high dimensional datasets to evaluate the potential and the effectiveness of seven well-known methods in terms of accuracy, purity and normalised mutual information.