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K-bMOM is a clustering algorithm that manages to give relevant clusterings while data are polluted with isolated datapoints (not visible on the picture).
You can see on the left that their algorithm K-bMOM (bottom right clustering) does much better than K-medians, K-means and the less known K-PDTM, because the 5 groups of data are well recovered.
The Journal of American Statistical Association (JASA) rejected the article with reviewer comentars. We all hope their work will be accepted in the Computational Statistics and Data Analysis journal (CSDA). Until this second verdict, you can find the current version of there article on arXiv (or directly here) and a fournished explanation here.