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Evaluating the Performance of the Students in the Technical Education through Data Mining Technique

-In order to understand how and why data mining works, it’s important to understand a few fundamental concepts. First, data mining relies on four essential methods: Classification, categorization, estimation, and visualization. Classification identifies associations and clusters, and separates subject under study. Categorization uses rule induction algorithms to handle categorical outcomes, such as “persist” or “dropout,” and “transfer” or “stay”. Visualization uses interactive graphs to demonstrate mathematically induced rules and scores, and is far more sophisticated than pie or bar charts. Visualization is used primarily to depict three-dimensional geographic locations of mathematical coordinates. Higher education institutions can use classification, for a comprehensive analysis of student characteristics, or use estimation to predict the likelihood of a variety of outcomes, such as transferability, persistence, retention, and course success. In this paper we have evaluated about the performance of the students through their scores obtained in different years as well as their dropout percentage of each year.