|Institution:||University of Dayton|
|Degree:||Master of Computer Science (M.C.S.)|
|Keywords:||Computer Science; Dataming; Genetic Agortihem; Eduction;|
|Full text PDF:||http://rave.ohiolink.edu/etdc/view?acc_num=dayton1399044144|
Big data is growing in importance in everyday life, yet traditional models of University education do not make good use of it. This thesis proposes a system that allows students to find courses based on similarity measures and take these courses in an exam-based environment. We describe a new mining method that can efficiently search, cluster and perform related functions in the system. The basic idea of this mining is to map courses in a university to buildings in a city. This means that finishing a degree or getting a skill is analogous to finding a path in the city. A number of approaches to build the city are presented. In the process of developing an algorithm, we use machine learning, artificial intelligence, and classic mining methods as core ideas.