Comparison of Cluster Validity Indices and Decision Rules for Different Degrees of Cluster Separation

Kaczynska, Sara;Marion, Rebecca;von Sachs, Rainer
(2020) European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning — Location: Bruges, Belgium (2.October.2020)

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Clustering algorithms are powerful tools for data exploration but often require an a priori choice of the number of clusters. In practice, cluster validity indices (CVIs) are used to quantify the clustering structure of candidate partitions, then decision rules are applied to the indices to choose the best number of clusters. This study analyzes how dimensionality and the degree of cluster separation impact the choice of the number of clusters according to 7 different indices and various decision rules. In contrast to previous studies, the degree of cluster separation is controlled by a single parameter and several decision rules are tested for each CVI.
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Kaczynska, S., Marion, R., & von Sachs, R. (2020). Comparison of Cluster Validity Indices and Decision Rules for Different Degrees of Cluster Separation. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium. https://hdl.handle.net/2078.5/220658