Tk-Merge: Computationally Efficient Robust Clustering Under General Assumptions

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 the presence of data contamination. Its generalizations and an adaptive procedure to estimate the amount of contamination are also presented.