Quantitative evaluation of internal clustering validation indices using binary datasets

Different clustering methods often classify the same dataset differently. Selecting the ‘best’ clustering solution out of a multitude of alternatives is possible with cluster validation indices. The behavior of validity indices changes with the structure of the sample and the properties of the clustering algorithm. Unique properties of each index cause increasing or decreasing performance in some conditions. Due to the large variety of cluster validation indices, choosing the most suitable index concerning the dataset and clustering algorithms is challenging. We aim to assess different internal clustering validation indices. In the present paper, the validity indices consist of geometric and non-geometric methods. For this purpose, we applied simulated datasets with different noise levels. Each dataset was repeated 20 times. Three clustering algorithms with Jaccard dissimilarity are used, and 27 clustering validation indices are evaluated. The results provide a reliability guideline for the selection cluster validity indices.