Superparamagnetic Clustering of Data

Marcelo Blatt, Shai Wiseman, and Eytan Domany
Phys. Rev. Lett. 76, 3251 – Published 29 April 1996
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Abstract

We present a new approach for clustering, based on the physical properties of an inhomogeneous ferromagnetic model. We do not assume any structure of the underlying distribution of the data. A Potts spin is assigned to each data point and short range interactions between neighboring points are introduced. Spin-spin correlations, measured (by Monte Carlo procedure) in a superparamagnetic regime in which aligned domains appear, serve to partition the data points into clusters. Our method outperforms other algorithms for toy problems as well as for real data.

  • Received 31 August 1995

DOI:https://doi.org/10.1103/PhysRevLett.76.3251

©1996 American Physical Society

Authors & Affiliations

Marcelo Blatt, Shai Wiseman, and Eytan Domany

  • Department of Physics of Complex Systems, The Weizmann Institute of Science, Rehovot 76100, Israel

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Issue

Vol. 76, Iss. 18 — 29 April 1996

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