Knowledge Representing and Clustering in e-Learning
For e-Learning, traditional navigator or searching engine has inherent weaknesses, so individualized intelligent learning is difficult to be realized. This paper proposed a hybrid knowledge structure reflecting the relationships among knowledge modules. A series of association knowledge items were gathered by standardized inputting and knowledge clustering based on association rules. Based on the mapping of knowledge items to knowledge domain, the proposed knowledge clustering and representation could intelligently provide learner clues of interrelated learning. The simulation results showed that the proposed plan is an effective scheme of intelligent learning.
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- 1.Fati, W., Kehang, H.: Research on intelligent multimedia distance education system based on multi-agent. In: Proceedings of ICCE 1998, pp. 250–257. Higher Education Publishing House, Beijing (1998)Google Scholar
- 2.RuiMin, S., YanQing, X.: Research on intelligent distant teaching environment based on multi-agent. Computer engineering and application, 102–108 (2002)Google Scholar
- 3.Qin-bao, S., Jun-yi, S.: A web document clustering algorithm based on association rule. Journal of Software, 317–474 (2002)Google Scholar
- 4.Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of ACM SIGMOD International conference on Management of Data, pp. 207–216 (1993)Google Scholar
- 5.Hidber, C.: Online Association Rule Mining. In: Proceedings of ACM SIGMOD International conference on Management of Data, pp. 145–156 (1999)Google Scholar
- 6.Agrawal, R., Srikant, R.: Fast alogorithm for mining association rules. In: Jorge, B.B., Matthias, J., Carlo, Z. (eds.) Proceedings of the 20th International Conference on Very Large Databases, pp. 487–499. Morgan Kaufmann Publishers, Inc., Santiago (1994)Google Scholar