Abstract
This article proposes a supervised approach to evaluate the contribution of explanatory variables to a clustering. The main idea is to learn to predict the instance membership to the clusters using each individual variable. All variables are then sorted with respect to their predictive power, which is measured using two evaluation criteria, i.e. accuracy (ACC) or Adjusted Rand Index (ARI). Once the relevant variables which contribute to the clustering discrimination have been determined, we filter out the redundant ones thanks to a supervised method. The aim of this work is to help end-users to easily understand a clustering of high-dimensional data. Experimental results show that our proposed method is competitive with existing methods from the literature.
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Ismaili, O.A., Lemaire, V., Cornuéjols, A. (2014). A Supervised Methodology to Measure the Variables Contribution to a Clustering. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_20
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DOI: https://doi.org/10.1007/978-3-319-12637-1_20
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