  K-means algorithm is a very popular clustering algorithm which is famous for
its simplicity. Distance measure plays a very important rule on the performance
of this algorithm. We have different distance measure techniques available. But
choosing a proper technique for distance calculation is totally dependent on
the type of the data that we are going to cluster. In this paper an
experimental study is done in Matlab to cluster the iris and wine data sets
with different distance measures and thereby observing the variation of the
performances shown.
