Data clustering and noise undressing of correlation matrices

Details Data clustering and noise undressing of correlation matrices Data clustering and noise undressing of correlation matrices

2000-03-14

arxiv.org/abs/cond-mat/0003241v1

Physics

Condensed Matter

Statistical Mechanics

4 pages, 3 figures

Scientific paper

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.

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