Thermodynamics-inspired unsupervised clustering of objects

The goal of unsupervised machine learning is to find the underlying structure that describes a dataset. Real-world applications include the clustering of customers by the ads they click on or the movies they watch. We treat unsupervised learning as an optimization problem by designing a morphism to transform data attributes into a mathematical graph and defining a graph entropy that is minimized when only relatively few nodes are highly connected. The thermodynamics of such system are derived and simulated annealing is used to cluster similar data together. The methodology is applied to network traffic patterns and scientific literature and results are discussed.