Theses and Dissertations


Archit Harsh

Issuing Body

Mississippi State University


Ball, John E.

Committee Member

Ramkumar, Mahalingham

Committee Member

Younan, Nicholas H.

Date of Degree


Original embargo terms

MSU Only Indefinitely

Document Type

Graduate Thesis - Campus Access Only


Electrical and Computer Engineering

Degree Name

Master of Science


James Worth Bagley College of Engineering


Department of Electrical and Computer Engineering


A non-parametric data clustering technique for achieving efficient data-clustering and improving the number of clusters is presented in this thesis. K-Means and Expectation-Maximization algorithms have been widely deployed in data-clustering applications. Result findings in related works revealed that both these algorithms have been found to be characterized with shortcomings. K-Means was established not to guarantee convergence and the choice of clusters heavily influenced the results. Expectation-Maximization’s premature convergence does not assure the optimality of results and as with K-Means, the choice of clusters influence the results. To overcome the shortcomings, a fast automatic K-EM algorithm is developed that provide optimal number of clusters by employing various internal cluster validity metrics, providing efficient and unbiased results. The algorithm is implemented on a wide array of data sets to ensure the accuracy of the results and efficiency of the algorithm.