PREDICTING BEST TEACHERS BASED ON CLASSIFYING INTERESTING RULES IN MULTIDIMENSIONAL SCHEMA FORMATION

Abstract

Data mining offers many technologies to analyze and detect the hidden pattern and also convert raw data into useful information. Clustering multi-dimensional data cube is an important technique used to group the related elements without advance knowledge. Identifying the best teachers is an important task of teacher recruitment. Quality education depends largely on teachers performance evaluation and prediction is done based on clustering and decision making approaches. It allows the institution to generate the interested rule classification and determine whether teacher can be recruited or not. This paper concentrates the concept of multi dimensional Association Rule clustering for data prediction.