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arXiv · 2110.07521

Multi-objective Clustering: A Data-driven Analysis of MOCLE, MOCK and $\Delta$-MOCK

Adriano Kultzak·Cristina Y. Morimoto·Aurora Pozo·Marcílio C. P. de Souto
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Abstract

We present a data-driven analysis of MOCK, $\Delta$-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 $\Delta$-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.

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Adriano Kultzak, Cristina Y. Morimoto, Aurora Pozo, Marcílio C. P. de Souto. 2021-10-14. Multi-objective Clustering: A Data-driven Analysis of MOCLE, MOCK and $\Delta$-MOCK. https://arxiv.org/abs/2110.07521

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