Data clustering and noise undressing of correlation matrices

Preprint

14 March 2000

preprint
Published by arXiv in arXiv

https://doi.org/10.48550/arXiv.cond-mat/0003241

Abstract

We discuss a new approach to data clustering. We find that maximum likelyhood leads naturally to an Hamiltonian of Potts variables which depends on the correlation matrix and whose low temperature behavior describes the correlation structure of the data. For random, uncorrelated data sets no correlation structure emerges. On the other hand for data sets with a built-in cluster structure, the method is able to detect and recover efficiently that structure. Finally we apply the method to financial time series, where the low temperature behavior reveals a non trivial clustering.

Keywords

CORRELATION MATRICES
TEMPERATURE BEHAVIOR
CORRELATION MATRIX
NOISE UNDRESSING
LOW TEMPERATURE
DATA CLUSTERING
UNDRESSING
HAND

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Version 1, 2000-03-14, ArXiv

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