  We address general-shaped clustering problems under very weak parametric
assumptions with a two-step hybrid robust clustering algorithm based on trimmed
k-means and hierarchical agglomeration. The algorithm has low computational
complexity and effectively identifies the clusters also in presence of data
contamination. We also present natural generalizations of the approach as well
as an adaptive procedure to estimate the amount of contamination in a
data-driven fashion. Our proposal outperforms state-of-the-art robust,
model-based methods in our numerical simulations and real-world applications
related to color quantization for image analysis, human mobility patterns based
on GPS data, biomedical images of diabetic retinopathy, and functional data
across weather stations.
