International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 10 | Oct -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 968 Data Clustering in Education for Students Shashikant Pradip Borgavakar, Mr. Amit Shrivastava, Prof. Preetesh Purohit 1 Research Scholar, Computer Science & Engineering Department, Swami Vivekanand College of Engineering Indore, India 2 Asst. Professor, Computer Science & Engineering Department, Swami Vivekanand College of Engineering Indore, India 3 Professor, Head of Computer Science and Engineering Department, Swami Vivekananda College of Engineering, Indore, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract –Data clustering used to maintain significant relationships for large data set or warehouse by using extraction of data. We are analyzing student behavior by using data clustering technique means K-Means Clustering. Class mid and final exam, assignment, credit test by using this factor we are evaluating studied. To evaluate accurate result we are required to correct student mark detail and it was strongly recommended that to get correct result we are required correct input data. This study will help professor to reduce the failed ratio to significant level and improve the performance of students. Key Words: K-means, Database, Student evaluation etc. 1. INTRODUCTION Data clustering is process of extracting large data which are valid, hidden pattern and useful data. Day to day data which are store for education is increasing rapidly. For future prediction clustering technique is most significant technique. Here we are partitioning homogeneous groups with their characteristics and performance to evaluate result. This application can help both instructor and student to enhance the education quality. This study makes large data set of students into groups of student by suing their characteristics. 2. LITERATURE SURVEY Irjet Template sample paragraph .Define abbreviations and acronyms the first time they are used in the text, even after they have been defined in the abstract. Abbreviations such as IEEE, SI, MKS, CGS, sc, dc, and rms do not have to be defined. Do not use abbreviations in the title or heads unless they are unavoidable. Table -1: Sample Table format Research Paper Improving the Accuracy and Efficiency of the k- means Clustering Algorithm An Iterative Improved k-means Clustering Refining Initial Points for K-Means Clustering Comparison of various clustering algorithms Problem being addressed Lower accuracy and efficiency Number of Iterations are Less Estimate is fairly unstable due to elements of the tails appearing in the sample Which clustering algorithm is best Importance of the problem algorithm requires a time complexity Total number of iterations required by k-means and improved k- means is much larger Importanc e of the problem of having a good initial points Way of Process Gap in the prior work Accuracy and Efficiency is most complicate d to reducing Check multiple iterations To finding Initial Points Finding algorithm Specific research questions or research objective To Overcome the problem of Accuracy and Efficiency This paper presented iterative improved k- means clustering algorithm that makes the k-means more efficient and produce good quality clusters A fast and efficient algorithm for refining an initial starting point for a general class of clustering algorithms has been presented data mining is that to discover the data and patterns and store it in an understandabl e form Broad outline of how the author solved the problem Using K- Means clustering Algorithm and The enhanced Method Iteration improve k- means cluster algorithm Using Clustering Cluster Applied DBSCAN and OPTICS algorithms Details of implementatio n of procedure Phase 1 of the enhanced algorithm requires a time complexity of O(n2) for finding the initial centroids, as the maximum time required here is for computing Dividing number of parts then calculate centers and decide membershi p of patterns then repeat same steps Results on Real Word Data All clustering algorithm process and find