The Poisson-Influenced K-Means Algorithm, a Maximum-Likelihood Procedure for Clusters with a Known Probability Distribution
Zachary H. Levine, Brian P. Morris
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 a number of 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.
and Morris, B.
The Poisson-Influenced K-Means Algorithm, a Maximum-Likelihood Procedure for Clusters with a Known Probability Distribution, The Mathematica Journal, [online], https://doi.org/10.3888/tmj.18-3
(Accessed September 25, 2023)