Towards cluster validity index evaluation and selection
Andrey A. Filchenkov, Sergey Muravyov, Vladimir Parfenov · Artificial Intelligence and Natural Language · 2016
In this work, we address the hard clustering problem. We study how well clustering algorithm efficacy measures (clustering validity indices) cao rellect the clustering quality. We use assessors' estimations for cluster partition adequacy as the ground truth and explain, why tbis is the only measure that cao be used in tbis quality. We compare different clustering validity indices and show that none of them can be the universal, relleeting quality for each cluster partition. To do so, we introduce four quality measures for CVI evaluation. Also, we suggest an approach for the best CVI predietion for a given dataset based on meta-lesrning.