We introduce a robust and sparse clustering procedure for high-dimensional data. The robustness aspect is addressed by a weighting function incorporated in the k-means procedure, consequently leading to an automatic weight assignment for each observation. The sparsity aspect is given by a lasso-type penalty on weighted between-cluster sum of squares. We additionally propose a framework for determining the optimal number of both clusters and variables that contribute to a cluster separation.
S. Brodinova, P. Filzmoser, T. Ortner, M. Zaharieva, C. Breiteneder: "Robust and sparse clustering for high-dimensional data"; Talk: Conference of the CLAssification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), Milan, Italy; 09-13-2017 - 09-15-2017; in: "CLADAG 2017 Book of Short Papers", (2017), ISBN: 978-88-99459-71-0.
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