Assessing Teacher’s Performance Evaluation and Prediction Model Using Cloud Computing Over Multi-dimensional Dataset

The main objective is to create a secured classifier for datasets based on clustering algorithm. K-means algorithm is one of the efficient techniques for mining large databases based on cloud computing platform to store large database with least cost. Cloud computing allows users to outsource their data. For multi-dimensional data the clustering technique is implemented which performs clustering of related elements without advance knowledge. The K-nearest neighbor classification is analyzed by using dataset under different conditions of parameters. In view of the above, the development of data management with a cloud computing is gaining more attention towards multi-dimensional datasets. It is a challenging task to obtain secured data in evolution of data mining technique based on cloud computing employed using classifier techniques. Quality of education depends largely on teacher’s ability, performance, knowledge, assessment and prediction on the basis of data mining techniques and clustering. These approaches permit the educational institution to decide and evaluate the classification rule to determine and recruit the best teacher based on knowledge by using cloud database which is a challenging task. The proposed technique provides secured cloud computing details regarding teacher’s recruitment, privacy of user’s input query, selecting the best teacher and hides the access patterns on cloud. The proposed idea is computed by extracting the data and proves that it provides better accuracy for selecting the best teachers and also improves the speed and constancy of recruitment application. The teacher’s recruitment is used in evaluating the ranks based on performance so that, the institution takes a better decision for recruitment.