  Among all the partition based clustering algorithms K-means is the most
popular and well known method. It generally shows impressive results even in
considerably large data sets. The computational complexity of K-means does not
suffer from the size of the data set. The main disadvantage faced in performing
this clustering is that the selection of initial means. If the user does not
have adequate knowledge about the data set, it may lead to erroneous results.
The algorithm Automatic Initialization of Means (AIM), which is an extension to
K-means, has been proposed to overcome the problem of initial mean generation.
In this paper an attempt has been made to compare the performance of the
algorithms through implementation

Machine Learning