Multi-objective Clustering: A Data-driven Analysis of MOCLE, MOCK and $\Delta$-MOCK
Machine Learning
2021-10-26 v2
Abstract
We present a data-driven analysis of MOCK, -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 -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.
Keywords
Cite
@article{arxiv.2110.07521,
title = {Multi-objective Clustering: A Data-driven Analysis of MOCLE, MOCK and $\Delta$-MOCK},
author = {Adriano Kultzak and Cristina Y. Morimoto and Aurora Pozo and Marcílio C. P. de Souto},
journal= {arXiv preprint arXiv:2110.07521},
year = {2021}
}
Comments
Submitted to ICONIP 2021