We present a novel approach for measuring feature importance in k-means clustering, or variants thereof, to increase the interpretability of clustering results. In supervised machine learning, feature importance is a widely used tool to ensure interpretability of complex models. We adapt this idea to unsupervised learning via partitional clustering. Our ...

The goal is to compare a few algorithms for missing imputation when used before k-means clustering is performed. For the latter we use the same algorithm as in ClustImpute to ensure that only the computation time of the imputation is compared. In a nutshell, we’ll se that ClustImpute scales ...

We are happily introducing a new k-means clustering algorithm that includes a powerful multiple missing data imputation at the computational cost of a few extra random imputations (benchmarks following in a separate article). More precisely, the algorithm draws the missing values iteratively based on the current cluster assignment so that ...