Analyzing Student Moral Educational Behavior Pattern using Fuzzy K-means clustering with Evolutionary Pattern Mining Approach

The continuous population and growth of educational informatization numerus educational data are stored in university database that poses a challenging to every management of universities. In the big data mining environment, educational data is a significant role and university data is a huge proportion of education data. This paper proposed a Fuzzy K-means (FKM) clustering with Evolutionary Pattern Mining (EPM) approach for the analysis of student moral education behavior pattern. The basic information about the students like name, register number, institute name, contact number, grade is collected initially. The data is preprocessed through normalization which enhances the model performance. Then, the preprocessed data is provided to FKM clustering and EPM approach for student moral education behavior pattern analysis. The proposed model is estimated through accuracy, precision, recall and f1-score. The FKM clustering with EPM attains better results like 98.61% accuracy, 0.991 of precision, 0.996 of recall and 0.992 of f1-score when compared to existing methods like Inductive Visual Miner (IVM), Random Forest (RF) and K-Nearest Neighbors (KNN).