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

TL;DR

This paper provides a comprehensive data-driven comparison of three multi-objective clustering methods, analyzing their performance across diverse datasets and uncovering the reasons behind their strengths and weaknesses.

Contribution

It offers the first detailed comparative analysis of MOCLE, MOCK, and $ riangle$-MOCK, highlighting their relative performance and underlying behavior.

Findings

$ riangle$-MOCK outperforms others on certain datasets

MOCLE and MOCK show complementary strengths

Insights into method-specific advantages and limitations

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.