In 1961 Herbert Simon and Albert Ando [Econometrika, 29 (1961), pp. 111–138] published the theory behind the long-term behavior of a dynamical system that can be described by a nearly uncoupled matrix. Over the past ﬁfty years this theory has been used in a variety of contexts, including queueing theory, brain organization, and ecology. In all of these applications, the structure of the system is known and the point of interest is the various stages the system passes through on its way to some long-term equilibrium. This paper looks at this problem from the other direction. That is, we develop a technique for using the evolution of the system to tell us about its initial structure, and then use this technique to develop an algorithm that takes the varied solutions from multiple data clustering algorithms to arrive at a single data clustering solution.
This is the publisher’s version of the work. This publication appears in Gettysburg College’s institutional repository by permission of the copyright for personal use, not for redistribution.
Meyer, Carl D. and Charles D. Wessell. “Stochastic Data Clustering.” SIAM J. Matrix Analysis and Application 33-4 (2012): 1214-1236.