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

arXiv: https://arxiv.org/abs/2110.07521 · HTML: https://mlsanity.com/paper/2110.07521/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 28 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Built on by (most cited first, 1 of 1)

- [An Analysis of the Admissibility of the Objective Functions Applied in Evolutionary Multi-objective Clustering](https://mlsanity.com/paper/2206.09483/) (2022)

## Abstract (arXiv)

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.