Temporal Approaches in Data Mining. A Case Study in Agricultural Environment

1. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proc. of the ACM SIGMOD Int. Conf. on Management of Data, Washington, D.C., May 26–28, pp. 207–216. ACM Press, New York (1993)

   Google Scholar 

2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. of 20th Int. Conf. on Very Large Data Bases (VLDB 1994), Santiago de Chile, Chile, September 12–15, pp. 487–499. Morgan Kaufmann, San Francisco (1994)

   Google Scholar 

3. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Yu, P.S., Chen, A.L.P. (eds.) Proc. of the 11th Int. Conf. on Data Engineering, Taipei, Taiwan, March 6–10, pp. 3–14. IEEE Computer Society, Los Alamitos (1995)

   Chapter  Google Scholar 

4. Ale, J.M., Rossi, G.H.: An approach to discovering temporal association rules. In: Proc. of the ACM Symposium on Applied Computing, Villa Olmo, Italy, March 19–21, pp. 294–300. ACM, New York (2000)

   Chapter  Google Scholar 

5. Bettini, C., Wang, X.S., Jajodia, S.: Testing complex temporal relationships involving multiple granularities and its application to data mining. In: Proc. of the 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Montreal, Canada, June 3–5, pp. 68–78. ACM Press, New York (1996)

   Google Scholar 

6. Bosch, A., Torres, M., Maríin, R.: Reasoning with disjunctive fuzzy temporal constraint networks. In: Proc. of the 9th Int.Symposium on Temporal Representation and Reasoning (TIME 2002), pp. 36–43. IEEE Computer Society, Los Alamitos (2002)

   Chapter  Google Scholar 

7. Cañadas, J.J., del Águila, I.M., Bosch, A., Túnez, S.: An intelligent system for therapy control in a distributed organization. In: Shafazand, H., Tjoa, A.M. (eds.) EurAsia-ICT 2002. LNCS, vol. 2510, pp. 19–26. Springer, Heidelberg (2002)

   Chapter  Google Scholar 

8. Cheung, D.W., Han, J., Ng, V., Wong, C.Y.: Maintenance of discovered association rules in large databases: An incremental updating technique. In: Stanley, Y.W. (ed.) Proc. of the 12th Int. Conf. on Data Engineering, New Orleans, Louisiana, February 26 – March 1, pp. 106–114. IEEE Computer Society, Los Alamitos (1996)

   Google Scholar 

9. Fayyad, U., Piatetky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AIMagazine 17(3), 37–54 (1996)

   Google Scholar 

10. Joshi, M.V., Karypis, G., Kumar, V.: A universal formulation of sequential patterns. In: Proc. of the KDD’2001 Workshop on Temporal Data Mining, 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining San Fransisco. ACM, New York (August 2001)

    Google Scholar 

11. Bayardo Jr., R.J.: Efficiently mining long patterns from databases. In: Haas, L.M., Tiwary, A. (eds.) Proc. of the ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 1998), Seattle, Washington, USA, June 2–4, pp. 85–93. ACM Press, New York (1998)

    Chapter  Google Scholar 

12. Bayardo Jr., R.J., Agrawal, R., Gunopulos, D.: Constraint-based rule mining in large, dense databases. In: Proc. of the 15th Int. Conf. on Data Engineering, Sydney, Australia, March 23–26, pp. 188–197. IEEE Computer Society, Los Alamitos (1999)

    Google Scholar 

13. Kuok, C.M., Fu, A.W.C., Wong, M.H.: Mining fuzzy association rules in databases. SIGMOD Record 27(1), 41–46 (1998)

    Article  Google Scholar 

14. Lee, J.W., Lee, Y.J., Kim, H.K., Hwang, B.H., Ryu, K.H.: Discovering temporal relation rules mining from interval data. In: Shafazand, H., Tjoa, A.M. (eds.) EurAsia-ICT 2002. LNCS, vol. 2510, pp. 57–66. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

15. Li, Y., Ning, P., Wang, X.S., Jajodia, S.: Discovering calendar-based temporal association rules. Data & Knowledge Engineering 44, 193–218 (2003)

    Article  Google Scholar 

16. Lee, C.H., Lin, C.R., Chen, M.S.: On mining general temporal association rules in a publication database. In: Cercone, N., Lin, T.Y., Wu, X. (eds.) Proc. of the 2001 IEEE Int. Conf. on Data Mining, San Jose, California, USA, November 29- December 2, pp. 337–344. IEEE Computer Society, Los Alamitos (2001)

    Google Scholar 

17. Lin, D., Kedem, Z.M.: Pincer-search: An efficient algorithm for discovering the maximum frequent set. IEEE Transactions on Knowledge and Data Engineering 14(3), 553–566 (2002)

    Article  Google Scholar 

18. Lu, H., Feng, L., Han, J.: Beyond intra-transaction association analysis: Mining multi-dimensional inter-transaction association rules. ACM Transactions on Information Systems (TOIS) 18(4), 423–454 (2000)

    Article  Google Scholar 

19. Lu, H., Han, J., Feng, L.: Stock movement and n-dimensional inter-transaction association rules. In: Proc. of the Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD 1998), Seattle, Washington, June 1998, pp. 121–127 (1998)

    Google Scholar 

20. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1(3), 259–289 (1997)

    Article  Google Scholar 

21. Mannila, H.: Local and global methods in data mining: Basic techniques and open problems. In: Widmayer, P., Triguero, F., Morales, R., Hennessy, M., Eidenbenz, S., Conejo, R. (eds.) ICALP 2002. LNCS, vol. 2380, pp. 57–68. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

22. Ordonez, C., Santana, C.A., de Braal, L.: Discovering interesting association rules in medical data. In: Gunopulos, D., Rastogi, R. (eds.) Proc. of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Dallas, Texas, USA, May 14, pp. 78–85 (2000)

    Google Scholar 

23. Özden, B., Ramaswamy, S., Silberschatz, A.: Cyclic association rules. In: Proc. of the 14th Int. Conf. on Data Engineering, Orlando, Florida, USA, February 23–27, pp. 412–421. IEEE Computer Society, Los Alamitos (1998)

    Chapter  Google Scholar 

24. Park, J.S., Chen, M.S., Yu, P.S.: An effective hash based algorithm for mining association rules. In: Carey, M.J., Schneider, D.A. (eds.) Proc. of the 1995 ACM SIGMOD Int. Conf. on Management of Data, San Jose, California, May 22–25, pp. 175–186. ACM, New York (1995)

    Chapter  Google Scholar 

25. Pinto, H., Han, J., Pei, J., Wang, K., Chen, Q., Dayal, U.: Multi-dimensional sequential pattern mining. In: Proc. of the ACM CIKM Int. Conf. on Information and Knowledge Management, Atlanta, Georgia, USA, November 5-10, pp. 81–88. ACM, New York (2001)

    Google Scholar 

26. Roddick, J.F., Spiliopoulou, M.: A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering 14(4), 750–767 (2002)

    Article  Google Scholar 

27. Srikant, R., Agrawal, R.: Mining generalized association rules. In: Dayal, U., Gray, P.M.D., Nishio, S. (eds.) Proc. of 21th Int. Conf. on Very Large Data Bases (VLDB 1995), Zurich, Switzerland, September 11–15, pp. 407–419. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

28. Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: Jagadish, H.V., Mumick, I.S. (eds.) Proc. of the 1996 ACM SIGMOD Int. Conf. on Management of Data, Montreal, Quebec, Canada, June 4-6, pp. 1–12. ACM Press, New York (1996)

    Chapter  Google Scholar 

29. Tung, A.K.H., Lu, H., Han, J., Feng, L.: Breaking the barrier of transactions: Mining inter-transaction association rules. In: Proc. of the 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 15-18, pp. 297–301. ACM Press, New York (1999)

    Chapter  Google Scholar 

30. Tung, A.K.H., Lu, H., Han, J., Feng, L.: Efficient mining of intertransaction association rules. IEEE Transactions on Knowledge and Data Engineering 15(1), 43–56 (2003)

    Article  Google Scholar 

31. Wang, W., Yang, J., Yu, P.S.: Efficient mining of weighted association rules (war). In: Proc. of the sixth ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Boston, MA, USA, August 20–23, pp. 270–274. ACM, New York (2000)

    Chapter  Google Scholar 

32. Yang, D.L., Pan, C.T., Chung, Y.C.: An efficient hash-based method for discovering the maximal frequent set. In: Proc. of the 25th Int. Computer Software and Applications Conference (COMPSAC 2001), Chicago, IL, USA, October 8–12, pp. 85–93. IEEE Computer Society, Los Alamitos (2001)

    Google Scholar 

33. Zhou, A., Zhou, S., Wen, J., Tian, Z.: An improved definition of multidimensional inter-transaction association rule. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 104–108. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

34. Zhou, Z.-H.: Three perspectives of data mining (book review). Artificial Intelligence 143, 139–146 (2003)

    Article  MATH  Google Scholar 

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