  A pairwise clustering approach is applied to the analysis of the Dow Jones
index companies, in order to identify similar temporal behavior of the traded
stock prices. To this end, the chaotic map clustering algorithm is used, where
a map is associated to each company and the correlation coefficients of the
financial time series are associated to the coupling strengths between maps.
The simulation of a chaotic map dynamics gives rise to a natural partition of
the data, as companies belonging to the same industrial branch are often
grouped together. The identification of clusters of companies of a given stock
market index can be exploited in the portfolio optimization strategies.
