K-means sensitivity to outliers? I'm studying K-means, and one important drawback of K-means is the lack of robustness to outliers. My question is: are there any cases when the lack of robustness to outliers may be considered not as a defect of K-means but as a virtue instead?
yes, in detecting abnormal cases.

In the k-means based outlier detection technique, the data are partitioned into k groups by assigning them to the closest cluster centers.

Once assigned we can compute the distance or dissimilarity between each object and its cluster center, and pick those with largest distances as outliers.

k-means clustering for Outlier detection