Recommendation of College English Education Information using Context-based Gustafson Kessel with Fuzzy Granular Clustering

An innovative thinking and the action capability of the students in the procedure of combination have become significantly unconstrained through deploying an initiative as well as enthusiasm of the scient and technology. With respect to better cope with a broad development of the network teaching data, the most accurate extraction of the users needed the education data. Nevertheless, the time and space allocation for the learning and the teaching, there is no significance of the instruction and direction for the self-directed learning for the students. Hence, this research proposes the Context-based Gustafson Kessel with Fuzzy Granular (CGKFG) clustering approach for the college English education information examination. A combination of fuzzy granular computing enables the model to significantly handle the ambiguity and fuzziness in the data, which is basic in teaching estimations, delivering the most significant clustering results. The experimental results demonstrates that the proposed CGKFG approach attains the superior accuracy of 0.93, precision of 0.91 respectively as compared to the existing approaches like GK and Fuzzy C Means (FCM) approach.