Multi-objective Clustering: A Data-driven Analysis of MOCLE, MOCK and $Δ$-MOCK
This work provides insights into the strengths and weaknesses of specific clustering algorithms for researchers in data mining and machine learning, but it is incremental as it compares existing methods without introducing new ones.
The paper analyzed the performance of three multi-objective clustering methods (MOCLE, MOCK, and Δ-MOCK) on 12 datasets, identifying which methods performed well or poorly and explaining the reasons behind these behaviors.
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.