Data Mining plays a vital role in information management technology. It is a computational process of finding patterns from large databases. It mainly focuses on extracting knowledge from the given or the available data. Different knowledge extracting tools are used. This tool is most common among every sector be it educational, organizational etc. Educational Sector can take advantage out of these tools in order to increase the quality of education. But the sad part is still in present educational systems are not using it. Higher education Institutions needs to know which student will enroll in which course, which student needs more assistance. In data mining users are facing the problem when database consists of large number of features and instances. These kinds of problem[s] could not be handled using decision trees alone or clustering technique alone. Because, decision trees depend upon the dataset used and the configuration of the trees. Similarly, clustering alone doesn't work for all kind of patterns. So in order to find that which technique is most suitable, in this paper we have evaluated the performance of both the algorithms. Educational data is mined and the algorithms are applied to it so as to predict the results.
Weka, EDM, Decision Trees, Clustering, KDD.
Ritika Saxena, Educational Data Mining: Performance Evaluation of Decision Tree and Clustering Techniques Using WEKA Platform, HCTL Open International Journal of Technology Innovations and Research (IJTIR), Volume 14, April 2015, eISSN: 2321-1814, ISBN (Print): 978-1-62951-946-3.