  The present work proposes hybridization of Expectation-Maximization (EM) and
K-Means techniques as an attempt to speed-up the clustering process. Though
both K-Means and EM techniques look into different areas, K-means can be viewed
as an approximate way to obtain maximum likelihood estimates for the means.
Along with the proposed algorithm for hybridization, the present work also
experiments with the Standard EM algorithm. Six different datasets are used for
the experiments of which three are synthetic datasets. Clustering fitness and
Sum of Squared Errors (SSE) are computed for measuring the clustering
performance. In all the experiments it is observed that the proposed algorithm
for hybridization of EM and K-Means techniques is consistently taking less
execution time with acceptable Clustering Fitness value and less SSE than the
standard EM algorithm. It is also observed that the proposed algorithm is
producing better clustering results than the Cluster package of Purdue
University.
