British Journal of Mathematics & Computer Science 2(2): 62-71, 2012 SCIENCEDOMAIN international www.sciencedomain.org ________________________________________________________________ _____________________________________ *Corresponding author: Email: omidiorasayo@yahoo.co.uk; An Exploratory Study of K-Means and Expectation Maximization Algorithms Adigun Abimbola Adebisi 1 , Omidiora Elijah Olusayo 1* and Olabiyisi Stephen Olatunde 1 1 Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria. Research Article Received 20 th December 2011 Accepted 26 th January 2012 Online Ready 23 rd March 2012 _______________________________________________________________________ Abstract In this paper, K-Means and Expectation-Maximization algorithms are part of the commonly employed methods in clustering of data in relational databases. Experiments conducted with both clustering algorithms revealed that both algorithms have been found to be characterized with shortcomings. The parameters considered in evaluating the results of findings are the number of iterations (no distinct convergence, 1), the computation time (not defined, 3.2s) and the memory space (not defined, 1.1MB) consumed at the point of convergence of both K-means and Expectation-Maximization algorithms respectively. The results obtained revealed that Expectation-Maximization algorithm’s quick and premature convergence cannot be said to have guaranteed optimality of results while K-means was found not to guarantee convergence. Though reasonable conclusion could be drawn from results obtained with Expectation-Maximization algorithm, its premature convergence may raise some questions of doubt with regards to reliability of results obtained. Keywords: K-Means, Expectation Maximization, Clustering, Student database. 1 Introduction The K-Means algorithm is a very popular algorithm for data clustering because of its simplicity. Originally developed for and applied to the task of vector quantization, k-means has been used in a wide assortment of applications. It has been proven to be a good approach to classify data. K- Means (KM) clustering is the classification of similar objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset share some common trait - often proximity according to some defined distance measure (MacQueen, 1967). Intuitively, patterns within a valid cluster are more similar to each other than they are to a