MoCham: Robust Hierarchical Clustering Based on Multi-objective Optimization
Tomáš Bartoň, Tomas Bruna, Pavel Kordík · 2016
Many clustering evaluation methods are computed as a ratio between two objectives, typically these objectives express the compactness of all clusters while trying to maximize the separation between individual clusters. However, the clustering process itself is typically implemented as a single objective problem: optimizing a linear combination of between-points closeness. We propose MoCham - a hierarchical clustering algorithm that uses a multi-objective optimization for finding the optimal data points to merge. Our results suggest that a careful candidate selection of Pareto dominating pairs leads to more robust clustering results.