Clustering Data Through an Analogy to the Potts Model
Abstract
A new approach for clustering is proposed. This method is based on an analogy to a physical model; the ferromagnetic Potts model at thermal equilibrium is used as an analog computer for this hard optimization problem . We do not assume any structure of the un(cid:173) derlying distribution of the data. Phase space of the Potts model is divided into three regions; ferromagnetic, super-paramagnetic and paramagnetic phases. The region of interest is that corresponding to the super-paramagnetic one, where domains of aligned spins ap(cid:173) pear. The range of temperatures where these structures are stable is indicated by a non-vanishing magnetic susceptibility. We use a very efficient Monte Carlo algorithm to measure the susceptibil(cid:173) ity and the spin spin correlation function. The values of the spin spin correlation function, at the super-paramagnetic phase, serve to identify the partition of the data points into clusters.
Cite
Text
Blatt et al. "Clustering Data Through an Analogy to the Potts Model." Neural Information Processing Systems, 1995.Markdown
[Blatt et al. "Clustering Data Through an Analogy to the Potts Model." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/blatt1995neurips-clustering/)BibTeX
@inproceedings{blatt1995neurips-clustering,
title = {{Clustering Data Through an Analogy to the Potts Model}},
author = {Blatt, Marcelo and Wiseman, Shai and Domany, Eytan},
booktitle = {Neural Information Processing Systems},
year = {1995},
pages = {416-422},
url = {https://mlanthology.org/neurips/1995/blatt1995neurips-clustering/}
}