This is an iterative procedure that involves first selecting the number of clusters to be found in the data set. Loop over all observations reassigning them to the group with the closest mean.
Recompute group means.
Iterate steps 2 and 3 until convergence. Often a hierarchical method is used to generate the seed points or initial partitioning. Try one of the hierarchical methods to generate initial means. Also try randomly selecting 3 of the points from the data set as seeds. Mixture Models for Cluster Analysis Using mixture models for cluster analysis is currently an active research area.