Automatic K-Expectation-Maximization (K-EM) Clustering Algorithm for Data Mining Applications
Ball, John E.
Younan, Nicholas H.
Date of Degree
Original embargo terms
MSU Only Indefinitely
Graduate Thesis - Open Access
Electrical and Computer Engineering
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.
Harsh, Archit, "Automatic K-Expectation-Maximization (K-EM) Clustering Algorithm for Data Mining Applications" (2016). Theses and Dissertations MSU. 828.