One of the main advantages of GMMs is their flexibility in modeling data that may not be well-represented by simpler clustering methods like K-means. GMMs can handle clusters of different shapes, sizes, and densities, making them particularly useful for the heterogeneous nature of catalytic processes. Additionally, GMMs provide a probabilistic framework, allowing for the estimation of the likelihood that a given data point belongs to a particular cluster, which can be useful for making more nuanced interpretations.
