Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance
This is an incremental improvement for researchers and practitioners needing faster clustering with acceptable accuracy.
The paper tackles the problem of slow clustering by hybridizing Expectation-Maximization and K-Means algorithms, resulting in consistently less execution time with acceptable clustering fitness and lower Sum of Squared Errors compared to standard EM and a benchmark package.
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.