Applying Data Mining Technologies to the Learning and Study Strategies Inventory

Chien-Chou  Shih; Ding-An Chiang; Sheng-Wei  Lai; Yen-Wei  Hu

doi:10.6180/jase.2007.10.4.11

Applying Data Mining Technologies to the Learning and Study Strategies Inventory

Computer Science and Information Engineering

Chien-Chou Shih This email address is being protected from spambots. You need JavaScript enabled to view it.¹ , Ding-An Chiang² , Sheng-Wei Lai² and Yen-Wei Hu³

¹Department of Information and Communication, Tamkang University, Tamsui, Taiwan 251, R.O.C.
²Department of Computer Science and Information Engineering, Tamkang University, Tamsui, Taiwan 251, R.O.C.
³Center for General Education and Core Curriculum, Tamkang University, Tamsui, Taiwan 251, R.O.C.

Received: December 29, 2006
Accepted: June 8, 2007
Publication Date: December 1, 2007

Download Citation: ||https://doi.org/10.6180/jase.2007.10.4.11

ABSTRACT

To understand the thoughts and behavior of a large group of people, surveys are often used to obtain objective data. They are generally composed of dozens of questions that can be quite time-consuming, and a hassle to complete. To avoid this, the classification charts of the decision tree were used to search for critical and relevant questions, whereas the association rule reduced the number of questions within related categories. This paper applies data mining technology, which achieved the above-said outcome from performance evaluation results.

Keywords: Data Mining, Decision Tree, Association Rule, LASSI

REFERENCES

[1] Quinlan, J. R, “Induction of Decision Tree,” Machine Learning, Vol. 1, pp. 81106 (1996).
[2] Quinlan, J. R, C4.5: Programs for Machine Learning, San Mateo, Calif.: Morgan Kaufmann (1993).
[3] Stuart Russell, Peter Norving, Artifical Intelligence a Modern Approach, Prentice-Hall Internation Editions.
[4] Agrawal, R., Imielinski, T. and Swami, A., “Mining Association Rules between Sets of Items in Large Databases,” Proceedings of the 1993 ACM SIGMOD Conferencen on Management of Data, Washington D.C., pp. 207216 (1993).
[5] Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. and Verkamo, A. I., Fast Discovery of Association Rules, Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, eds., AAAI/MIT Press, pp. 307 328 (1996).
[6] Brin, S., Motwani, R., Ullman, J. and Tsur, S., “Dynamic Itemset Counting and Implication Rule for Market Basket Data,” Proceedings of the 1997 SIGMOD Conference on Management of Data, pp. 255264 (1997).
[7] Han, J., Pei, J. and Yin, Y., “Mining Frequent Patterns without Candidate Generation,” Proceedings of the 2000 ACM SIGMOD Conference on Management of Data, Dallas, Texas, USA, pp. 112 (2000).
[8] Hipp, J., Güntzer, U. and Nakhaeizadeh, G., “Algorithms for Association Rule Mining – A General Survey and Comparison,” SIGKDD Explorations, Vol. 2, pp. 5864 (2000).
[9] Park, J. S., Chen, M. S. and Yu, P. S., “Using a HashBased Method with Transaction Trimming for Mining Association Rules,” IEEE Transactions on Knowledge and Data Engineering, Vol. 9, pp. 813825 (1997).
[10] Agrawal, R. and Srikant, R., “Mining Sequential Patterns,” Proceedings of the 11th International Conference on Data Engineering, Taipei, Taiwan, pp. 314 (1995).
[11] Srikant, R. and Agrawal, R., “Mining Sequential Patterns: Generalizations and Performance Improvements,” Proceedings of the 5th International Conference on Extending Database Technology, Avignon, France, pp. 317 (1996). (An extended version is the IBM Research Report RJ 9994).