Clusterwise linear regression, a supervised learning technique that aims at finding latent groups with distinct linear relationships, has numerous applications across diverse scientific and applied domains. However, it often leads to optimization …
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 …
We consider a classical regression model contaminated by multiple outliers arising simultaneously from *mean-shift* and *variance-inflation* mechanisms -- which are generally considered as alternative. Identifying multiple outliers leads to …