S.Ganga et al, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.7, July- 2014, pg. 579-584 © 2014, IJCSMC All Rights Reserved 579 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 3, Issue. 7, July 2014, pg.579 – 584 RESEARCH ARTICLE Performance of Students Evaluation in Education Sector Using Clustering K-Means Algorithms S.Ganga Dr. T.Meyyappan M.Phil Research Scholar Professor Dept. of Computer Science Dept. of Computer Science Alagappa University Alagappa University Karaikudi Karaikudi TamilNadu TamilNadu gangas8872@gmail.com meyyappantt@alagappauniv.ac.in Abstract — Data Mining is the crucial step to find out previously unknown information from huge relational databases. Many techniques and algorithms are there in data mining, namely Association rules, clustering, and classification and prediction techniques. Each of the techniques holds its particular characteristics and behavior. In this paper, the authors proposed a new method with prime focus on clustering technique. The database for the specific set of students was collected. The clustering is made on some detailed manner and the results were produced. The clustering algorithm used here is the K-Means clustering algorithm, to find the nearest possible and cluster the similar group. The advantage of the proposed methodology is that, the cluster groups can be controlled, modified and accessed with ease. The dataset experimented contained 180 records with 63 attributes. The cluster group enables the dataset to be visualized in multiple dimensions with ease of access. The experimental analysis showed how the K-Means algorithm can be used especially to improve engineering students performance in higher education. This document gives formatting instructions for authors preparing papers for publication in the Proceedings of an IEEE conference. The authors must follow the instructions given in the document for the papers to be published. You can use this document as both an instruction set and as a template into which you can type your own text. Keywords— K-Means Clustering, Data Mining, Cluster Groups, Nearest Neighbor, Relational Databases