Title :
Research and application of data-mining technique in timetable scheduling
Author :
Guo, Fangming ; Song, Hua
Author_Institution :
Acad. Adm., Wuhan Univ. of Technol., Wuhan, China
Abstract :
This paper introduces the “reinforcement learning algorithm”-based timetable scheduling model which can solve new problems encountered in timetable scheduling by altering timetable eigenvector and timetable scheduling action vector. At the same time, a timetable historical data mining system based on Naive Bayesian classification algorithm is designed and implemented. The result shows that knowledge base for timetable scheduling can be constructed quickly and efficiently by the Naive Bayesian classification algorithm.
Keywords :
Bayes methods; data mining; educational administrative data processing; eigenvalues and eigenfunctions; knowledge based systems; learning (artificial intelligence); pattern classification; scheduling; Naive Bayesian classification algorithm; knowledge base; reinforcement learning algorithm; timetable eigenvector; timetable historical data mining system; timetable scheduling; Algorithm design and analysis; Bayesian methods; Classification algorithms; Data mining; Education; Knowledge management; Learning; Paper technology; Scheduling algorithm; Spatial databases; Navie Bayesian classification; data mining; reinforcement learning;
Conference_Titel :
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6347-3
DOI :
10.1109/ICCET.2010.5486073
