參考文獻 |
References
1. Ahn, K.-I. (2012). Effective product assignment based on association rule mining in retail. Expert Systems with Applications, 39(16), 12551-12556.
2. Ale, J. M. & Rossi, G.H. (2000). An Approach to Discovering Temporal Association Rules. ACM.
3. Agrawal, R. & Srikant, R. (1995). Mining Generalized Association Rules. Department of Computer Science, University of Wisconsin.
4. Agrawal, R., Imielinski, T. & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM.
5. Chang, J. H. (2011). Mining weighted sequential patterns in a sequence database with a time-interval weight. Knowledge-Based Systems, 24(1), 1-9.
6. Chen, Y. (2003). Discovering time-interval sequential patterns in sequence databases. Expert Systems with Applications, 25(3), 343-354.
7. Hu, Y.-H., Huang, T. C.-K., Yang, H.-R., & Chen, Y.-L. (2009). On mining multi-time-interval sequential patterns. Data & Knowledge Engineering, 68(10), 1112-1127.
8. Lan, G.-C., Hong, T.-P., Tseng, V. S., & Wang, S.-L. (2014). Applying the maximum utility measure in high utility sequential pattern mining. Expert Systems with Applications, 41(11), 5071-5081.
9. Lee, D., Park, S.-H., & Moon, S. (2013). Utility-based association rule mining: A marketing solution for cross-selling. Expert Systems with Applications, 40(7), 2715-2725.
10. Lin, C.-W., & Hong, T.-P. (2011). Temporal data mining with up-to-date pattern trees. Expert Systems with Applications, 38(12), 15143-15150.
11. Lan, G.-C., Hong, T.-P., & Tseng, V. S. (2011). Discovery of high utility itemsets from on-shelf time periods of products. Expert Systems with Applications, 38(5), 5851-5857.
12. Lee, Y. J., Lee, J. W., Chai, D. J., Hwang, B. H., & Ryu, K. H. (2009). Mining temporal interval relational rules from temporal data. Journal of Systems and Software, 82(1), 155-167.
13. Li, D. & Deogun, J., S. (2005). Discovering Partial Periodic Sequential Association Rules with Time Lag in Multiple Sequences for Prediction. Department of Computer Science and Engineering, University of Nebraska-Lincoln, 332-341.
14. Li, Y., Ning, P., Wang, X. S. & Jajodia, S. (2003). Discovering Calendar-based Temporal Association Rules. Center for Secure Information Systems, George Mason University.
15. Lee, Y. J., Lee, J. W., Chai, D. J., Hwang, B. H., & Ryu, K. H. (2002). Discovering Temporal Relation Rules Mining from Interval Data. Springer-Verlag Berlin Heidelberg, 57-66.
16. Railean, I., Lenca, P., Moga, S., & Borda, M. (2013). Closeness Preference – A new interestingness measure for sequential rules mining. Knowledge-Based Systems, 44, 48-56.
17. Tsai, P. S. M., & Chen, C.-M. (2004). Mining interesting association rules from customer databases and transaction databases. Information Systems, 29(8), 685-696.
18. Tansel, A. U. & Imberman, S.P. (1998), Discovery of Association Rules in Temporal Databases. Department of Computer Engineering and Information Science, Bilkent University.
19. Winarko, E., & Roddick, J. F. (2007). ARMADA – An algorithm for discovering richer relative temporal association rules from interval-based data. Data & Knowledge Engineering, 63(1), 76-90.
20. Xiao, Y., Tian, Y., & Zhao, Q. (2014). Optimizing frequent time-window selection for association rules mining in a temporal database using a variable neighbourhood search. Computers & Operations Research, 52, 241-250.
21. Yiyong, R. Zhang and I. Kaku (2011), A new framework of mining association rules with time-windows on real-time transaction database. International Journal of Innovative Computing, Information and Control, 7, 3239–3253. |