New THE MATHEMATICA JOURNAL article:
A Maximum-Likelihood Procedure for Clusters with a Known Probability Distribution
by BRIAN P. M. MORRIS, ZACHARY H. LEVINE
ABSTRACT: We present an implementation of the Poisson-Influenced K-Means Algorithm (PIKA), first developed to characterize the output of a superconducting transition edge sensor (TES) in the few-photon-counting regime. The algorithm seeks to group data into several clusters that minimize their distances from their means, as in classical K-means clustering, but with the added knowledge that the cluster sizes should follow a Poisson distribution.
