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

TL;DR

This paper introduces a novel method for grouping fleet entities by modeling their behavior as regression models and applying community detection in a graph where edges represent validation error-based differences.

Contribution

It presents a new approach that uses regression models and community detection to effectively cluster entities based on behavioral similarity.

Findings

Effective grouping of entities demonstrated on synthetic data.

Proposed measures assess the quality of groupings.

Method identifies optimal number of clusters.

Abstract

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.