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