  We introduce a rich model for multi-objective clustering with lexicographic
ordering over objectives and a slack. The slack denotes the allowed
multiplicative deviation from the optimal objective value of the higher
priority objective to facilitate improvement in lower-priority objectives. We
then propose an algorithm called Zeus to solve this class of problems, which is
characterized by a makeshift function. The makeshift fine tunes the clusters
formed by the processed objectives so as to improve the clustering with respect
to the unprocessed objectives, given the slack. We present makeshift for
solving three different classes of objectives and analyze their solution
guarantees. Finally, we empirically demonstrate the effectiveness of our
approach on three applications using real-world data.
