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.
翻译:我们提出了对MOCK、$\Delta$-MOCK和MOCLE的数据驱动分析。这些是三个密切相关的方法,它们使用多目标优化组合组合。更具体地说,根据收集了12套显示不同特性的数据集,我们调查MOCLE和MOCK的绩效,与最近提出的$\Delta$-MOCK相比。除了进行定量分析,确定哪些方法对另一个方法表现良好/较差之外,我们还对这种行为发生的原因进行了更详细的分析。事实上,我们的分析结果为所调查方法的优缺点提供了有益的见解。