Simultaneous Supervised and Unsupervised Classification Modeling for Assessing Cluster Analysis and Improving Results Interpretability

In the unsupervised classification field, the unknown number of clusters and the lack of assessment and interpretability of the final partition by means of inferential tools denote important limitations that could negatively influence the reliability of the final results. In this work, we propose to combine unsupervised classification with supervised methods in order to enhance the assessment and interpretation of the obtained partition. In particular, the approach consists in combining of the clustering method k-means (KM) with logistic regression (LR) modeling to have an algorithm that allows an evaluation of the partition identified through KM, to assess the correct number of clusters, and to verify the selection of the most important variables. An application on real data is presented to better clarify the utility of the proposed approach.