Adriano Kultzak

Adriano Kultzak, Cristina Y. Morimoto, Aurora Pozo et al.

We present a data-driven analysis of MOCK, $Δ$-MOCK, and MOCLE. These are three closely related approaches that use multi-objective optimization for crisp clustering. More specifically, based on a collection of 12 datasets presenting different proprieties, we investigate the performance of MOCLE and MOCK compared to the recently proposed $Δ$-MOCK. Besides performing a quantitative analysis identifying which method presents a good/poor performance with respect to another, we also conduct a more detailed analysis on why such a behavior happened. Indeed, the results of our analysis provide useful insights into the strengths and weaknesses of the methods investigated.