Robustification of the k-means clustering problem and tailored decomposition methods: when more conservative means more accurate

k-means clustering is a classic method of unsupervised learning with the aim of partitioning a given number of measurements into k clusters. In many modern applications, however, this approach suffers from unstructured measurement errors because the k-means clustering result then represents a clustering of the erroneous measurements instead of retrieving the true underlying clustering structure. We resolve this issue by applying techniques from robust optimization to hedge the clustering result against unstructured errors in the observed data. To this end, we derive the strictly and $$\Gamma $$ Γ -robust counterparts of the k-means clustering problem. Since the nominal problem is already NP-hard, global approaches are often not feasible in practice. As a remedy, we develop tailored alternating direction methods by decomposing the search space of the nominal as well as of the robustified problems to quickly obtain feasible points of good quality. Our numerical results reveal an interesting feature: the less conservative $$\Gamma $$ Γ -approach is clearly outperformed by the strictly robust clustering method. In particular, the strictly robustified clustering method is able to recover clusterings of the original data even if only erroneous measurements are observed.