Abstract

Institutions of higher education, community colleges, and even individual teachers are increasingly turning to electronic educational technology, often known as e-learning portals, to facilitate the exchange of knowledge and the development of a robust learning environment. The massive amounts of data collected by educational institutions need stringent measures to prevent data management and duplication. Data design should provide a methodical way to exchange and utilize existing data for this purpose. The goal of this work is to provide a method for automatically building a taxonomy from a collection of keywords that can be used for data sharing, reuse, and search purposes. The developed taxonomy must not be related to any other data categorization systems. Bayesian Rose Tree and K-mean closest neighbor classifier are two deployment methods used in taxonomy construction. Increasing the amount of discrete values will improve the classification accuracy of the data mining model. For m