This paper introduces KGMM, a method for estimating the score function that describes how probability density changes across state space. It combines bisecting K-means clustering, a Gaussian mixture representation, and neural interpolation of the resulting score estimates. The construction addresses noise amplification that can make conventional Gaussian mixture scores unreliable when component covariances are small. Examples involving potential systems and chaotic Lorenz-type models show that the estimated scores can support stochastic models with accurate long-term distributions. Readers get a concrete score-estimation workflow and an understanding of when clustering can provide a useful foundation for generative reduced-order models.