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This paper deals with grouping of entities in a fleet based on theirbehavior. The behavior of each entity is characterized by its historicaldataset, which comprises a dependent variable, typically a performance measure,and multiple independent variables, typically operating conditions. Aregression model built using this dataset is used as a proxy for the behaviorof an entity. The validation error of the model of one unit with respect to thedataset of another unit is used as a measure of the difference in behaviorbetween two units. Grouping entities based on their behavior is posed as agraph clustering problem with nodes representing regression models and edgeweights given by the validation errors. Specifically, we find communities inthis graph, having dense edge connections within and sparse connectionsoutside. A way to assess the goodness of grouping and finding the optimumnumber of divisions is proposed. The algorithm and measures proposed areillustrated with application to synthetic data.

Grouping Entities in a Fleet by Community Detection in Network of Regression Models