Clusters from higher order correlations

Given a set of variables and their mutual correlations, we develop a method for finding clustering among the variables. The method takes advantage of information implicit in higher-order (not just pairwise) correlations. The idea is to define a Potts model whose energy is based on the correlations. Each state of this model is a partition of the variables and a Monte Carlo method is used to identify states of lowest energy, those most consistent with the correlations. A set of the 100 or so lowest such partitions is then used to construct a stochastic dynamics (using the adjacency matrix of each partition) whose observable representation gives the clustering. Four examples are studied. For three of them the 3rd-order correlations are significant for getting the clusters right. One of these is a toy model of a biological system in which the joint action of several genes or proteins is necessary to accomplish a given process.