John H. Wolfe is the inventor of model-based clustering for continuous data.
in mathematics from Caltech and then went to graduate school in psychology at the University of California, Berkeley to work with Robert Tryon.
Around 1959, Paul Lazarsfeld visited Berkeley and gave a lecture on his latent class analysis, which fascinated Wolfe, and led him to start thinking about how one could do the same thing for continuous data.
After graduating from Berkeley, Wolfe took a job with the US Navy in San Diego first as a computer programmer and then as an operations research analyst.
[5][3] He used the mixture of multivariate normal distributions model, estimated it by maximum likelihood using a Newton-Raphson algorithm and gave the expression for the posterior probabilities of membership in each cluster.