Abstract
The incorporation of temporal semantic into the traditional data mining techniques has caused the creation of a new area called Temporal Data Mining. This incorporation is especially necessary if we want to extract useful knowledge from dynamic domains, which are time-varying in nature. However, this process is computationally complex, and therefore it poses more challenges on efficient processing that non-temporal techniques. Based in the inter-transactional framework, in [11] we proposed an algorithm named TSET for mining temporal patterns (sequences) from datasets which uses a unique tree-based structure for storing all frequent patterns discovered in the mining process. However, in each data mining process, the algorithm must generate the whole structure from scratch. In this work, we propose an extension which consists in the reusing of structures generated in previous data mining process in order to reduce the execution time of the algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aggarwal, C.C.: Towards long pattern generation in dense databases. SIGKDD Explorations 3(1), 20–26 (2001)
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)
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)
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)
Ale, J.M., Rossi, G.H.: An approach to discovering temporal association rules. In: Proc. of the 2000 ACM Symposium on Applied Computing, Villa Olmo, Via Cantoni 1, 22100 Como, Italy, March 19-21, pp. 294–300. ACM, New York (2000)
Bayardo, 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)
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)
Coenen, F., Goulbourne, G., Leng, P.: Tree structures for mining association rules. Data Mining and Knowledge Discovery 8, 25–51 (2004)
Fayyad, U., Piatetky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AIMagazine 17(3), 37–54 (1996)
Feng, L., Yu, J.X., Lu, H., Han, J.: A template model for multidimensional inter-transactional association rules. The VLDB Journal 11, 153–175 (2002)
Guil, F., Bosch, A., Marín, R.: TSET: An algorithm for mining frequent temporal patterns. In: Proc. of the First Int. Workshop on Knowledge Discovery in Data Streams, in conjunction with ECML/PKDD 2004, pp. 65–74 (2004)
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)
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)
Li, Y., Ning, P., Wang, X.S., Jajodia, S.: Discovering calendar-based temporal association rules. Data & Knowledge Engineering 44, 193–218 (2003)
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)
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. 12:1–12:7(1998)
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)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1(3), 259–289 (1997)
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)
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)
Pani, A.K.: Temporal representation and reasoning in artificial intelligence: A review. Mathematical and Computer Modelling 34, 55–80 (2001)
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)
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)
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)
Zhou, Z.H.: Three perspectives of data mining (book review). Artificial Intelligence 143, 139–146 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guil, F., Bailón, A., Bosch, A., Marín, R. (2005). An Iterative Method for Mining Frequent Temporal Patterns. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2005. EUROCAST 2005. Lecture Notes in Computer Science, vol 3643. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11556985_24
Download citation
DOI: https://doi.org/10.1007/11556985_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29002-5
Online ISBN: 978-3-540-31829-3
eBook Packages: Computer ScienceComputer Science (R0)