A method to validate clustering partitions

Frigau, Luca;Contu, Giulia;Ortu, Marco;Carta, Andrea

2023-01-01

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Abstract

To evaluate the performance of clustering algorithms is challenging because typically the true classes are unknown. In this paper we propose a new cluster validity method that combines internal and relative criteria and employs Machine Learning algorithms to produce a relative validity ranking of partitions obtained from different clustering algorithms. Compared to other methods, the proposed approach considers the features’ structure explicitly, can handle high-dimensional data, and can be applied to various clustering algorithms. The method has been tested on a simulated benchmark dataset, demonstrating its ability to rank correctly 11 classical clustering algorithms.