US Patent 10586165 Distributable clustering model training system

A computing system trains a clustering model. A responsibility parameter vector is initialized for each observation vector that includes a probability value of a cluster membership in each cluster. (A) Beta distribution parameter values are computed for each cluster. (B) Parameter values are computed for a normal-Wishart distribution for each cluster. (C) Each responsibility parameter vector defined for each observation vector is updated using the computed beta distribution parameter values, the computed parameter values for the normal-Wishart distribution, and a respective observation vector of the plurality of observation vectors. (D) A convergence parameter value is computed. (E) (A) to (D) are repeated until the computed convergence parameter value indicates the responsibility parameter vector defined for each observation vector is converged. A cluster membership is determined for each observation vector using a respective, updated responsibility parameter vector. The determined cluster membership is output for each observation vector.