Optimality-Based Clustering: An Inverse Optimization Approach

DOI10.1016/J.ORL.2021.12.012arXiv2107.05351MaRDI QIDQ6372570

Publication date: 12 July 2021

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.

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of mathematical programming (90C90) Linear programming (90C05) Pattern recognition, speech recognition (68T10)

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