in education domain, We can mine the hidden knowledge in the available databases for generating various analytical reports for proper decision making [10]. Grade Point Average (GPA) is commonly used as an indicator of academic performance [11]. An academic performance evaluation is a basic way to evaluate the progression of student performance, when evaluating student’s academic performance, there are occasion where the student data is grouped especially when the amounts of data is large. Thus, the pattern of data relationship within and among groups can be revealed. Grouping data can be done by using clustering methods such as K-Means, K*(STAR) and the Fuzzy C-Means algorithms. Classifying students using conventional techniques cannot give the desired level of accuracy, while doing it with the use of computing techniques may prove to be beneficial. Clustering or grouping a set of data sets is a key procedure for data processing .It is an unsupervised technique that is used to arrange pattern data into clusters. This research work deals with two of the most representative clustering algorithms namely centroid and crisp values based Fuzzy C-Means, K*(STAR) and represent object based on calculation of membership function. Fuzzy C-Means are described and analyzed for a datasets. Based on experimental results the algorithms are compared regarding their clustering quality and their performance, which depends on the time complexity between the various numbers of clusters chosen by the end user. The total elapsed time to cluster all the datasets and Clustering time for each cluster are also calculated and the results compared with one another [7].

K*(STAR) Algorithm, Fuzzy C-Means Algorithm, cluster Analysis, fuzzy logic

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