The Poisson option is designed to be applied to data containing mixtures of Poisson-distributed variables. The data is first normalized so all variables have the same means and variances, allowing the calculation of the distance metric without biasing the result in favor of larger-magnitude variables. The EM algorithm is then applied with a probability metric based on the likelihood function of the Poisson distribution function. As in the Gaussian Mixture Model option, rows are assigned to the nearest cluster with a probabilistic weighting. At the end of the EM iteration, the data is unnormalized and saved as a potential result, until or unless replaced by the next iteration.