  In this paper, we explore the detection of clusters of stocks that are in
synergy in the Indian Stock Market and understand their behaviour in different
circumstances. We have based our study on high frequency data for the year
2014. This was a year when general elections were held in India, keeping this
in mind our data set was divided into 3 subsets, pre-election period: Jan-Feb
2014; election period: Mar-May 2014 and :post-election period: Jun-Dec 2014. On
analysing the spectrum of the correlation matrix, quite a few deviations were
observed from RMT indicating a correlation across all the stocks. We then used
mutual information to capture the non-linearity of the data and compared our
results with widely used correlation technique using minimum spanning tree
method. With a larger value of power law exponent {\alpha}, corresponding to
distribution of degrees in a network, the nonlinear method of mutual
information succeeds in establishing effective network in comparison to the
correlation method. Of the two prominent clusters detected by our analysis, one
corresponds to the financial sector and another to the energy sector. The
financial sector emerged as an isolated, standalone cluster, which remain
unaffected even during the election periods.
