This document discusses using machine learning algorithms like clustering and Gaussian mixture modeling to analyze turbulence data and group it into meaningful partitions that align with human understanding of turbulence physics. The goal is to improve turbulence modeling simulations by decomposing complex datasets into interpretable clusters that reveal qualitative insights about turbulence types and enable quantitative analysis by identifying representative prototypes from each cluster. Technical challenges include finding a feature space for clustering that incorporates physical awareness and developing an approach that combines greedy feature search with clustering to produce partitions.