Clustering by Shift

In order to yield a more balanced partitioning, we investigate the use of additive regularizations for the Min Cut cost function, instead of normalization. In particular, we study the case where the regularization term is the sum of the squared size of the clusters, which then leads to shifting (adaptively) the pairwise similarities. We study the connection of such a model with Correlation Clustering and then propose an efficient local search optimization algorithm to solve the new clustering problem. Finally, we demonstrate the superior performance of our method by extensive experiments on different datasets.