Optimality-Based Clustering: An Inverse Optimization Approach

TL;DR

This paper introduces an innovative clustering method that groups data points based on underlying decision-making preferences by inferring common objective functions through inverse optimization, demonstrated in diet recommendation tasks.

Contribution

It presents a novel optimality-based clustering framework that infers shared objective functions from data, advancing clustering techniques with an inverse optimization perspective.

Findings

Three clustering models proposed and tested.

Effective in identifying decision-making preferences.

Applied successfully to diet recommendation data.

Abstract

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.