  In high energy physics experiments, calorimetric data reconstruction requires
a suitable clustering technique in order to obtain accurate information about
the shower characteristics such as position of the shower and energy
deposition. Fuzzy clustering techniques have high potential in this regard, as
they assign data points to more than one cluster,thereby acting as a tool to
distinguish between overlapping clusters. Fuzzy c-means (FCM) is one such
clustering technique that can be applied to calorimetric data reconstruction.
However, it has a drawback: it cannot easily identify and distinguish clusters
that are not uniformly spread. A version of the FCM algorithm called dynamic
fuzzy c-means (dFCM) allows clusters to be generated and eliminated as
required, with the ability to resolve non-uniformly distributed clusters. Both
the FCM and dFCM algorithms have been studied and successfully applied to
simulated data of a sampling tungsten-silicon calorimeter. It is seen that the
FCM technique works reasonably well, and at the same time, the use of the dFCM
technique improves the performance.
