  We propose a new clustering approach, called optimality-based clustering,
that clusters data points based on their latent decision-making preferences. We
assume that each data point is a decision generated by a decision-maker who
(approximately) solves an optimization problem and cluster the data points by
identifying a common objective function of the optimization problems for each
cluster such that the worst-case optimality error is minimized. We propose
three different clustering models and test them in the diet recommendation
application.
