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Proceedings Paper

Using an improved association rules mining optimization algorithm in web-based mobile-learning system
Author(s): Yin Huang; Jianhua Chen; Shaojun Xiong
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Paper Abstract

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

Paper Details

Date Published: 11 July 2009
PDF: 6 pages
Proc. SPIE 7490, PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering, 74902D (11 July 2009); doi: 10.1117/12.836637
Show Author Affiliations
Yin Huang, Wuhan Univ. (China)
Jianhua Chen, Wuhan Univ. (China)
Shaojun Xiong, HuaZhong Normal Univ. (China)


Published in SPIE Proceedings Vol. 7490:
PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering
Honghua Tan; Qi Luo, Editor(s)

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