Education data mining is one of the new emerging research areas in intra data mining domain. The main objective of applying data mining to educational data is to analyse educational data contents, models to summarize/analyse the learner?s discussions, etc. Education data mining concentrates on the computing process models which focus on education context. Researchers proposed a new approach in deriving new association rules for optimal learning sequence of students and tutors using K-means Clustering algorithm; here data?s are visualized and processed. The methodology increases the performance with the fast support calculation and other significant techniques are introduced to improve the efficiency of the association rule based mining process using K-means. The new approach is compared with Apriori algorithm and the comparison results presented here shows the algorithm is optimal than the traditional Apriori algorithm.
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