Abstract
This paper proposes a novel approach that extends the FP-tree in two ways. First, the tree is maintained to include every attribute that occurs at least once in the database. This facilitates mining with different support values without constructing several FP-trees to satisfy the purpose. Second, the tree is manipulated in a unique way that reflects updates to the corresponding database by scanning only the updated portion, thereby reducing execution time in general. Test results on two datasets demonstrate the applicability, efficiency and effectiveness of the proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Adnan, M., Alhajj, R., Barker, K.: Performance Analysis of Incremental Update of Association Rules Mining Approaches. In: Proceedings IEEE INES, Greece (September 2005)
Amir, Feldman, R., Kashi, R.: A New and Versatile Method for Association Generation. Information Systems 22(6), 333–347 (1999)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of ACM-SIGMOD, Washington D.C, pp. 207–216 (May 1993)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of VLDB, pp. 487–499 (September 1994)
Brijs, T., Swinnen, G., Vanhoof, K., Wets, G.: The Use of Association Rules for Product Assortment Decisions: A Case Study. In: Proceedings of ACM-KDD, San Diego, pp. 254–260 (1999)
Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. In: Proceedings of IEEE-ICDE, pp. 106–114 (1996)
Cheung, D.W., Ng, V.T., Tam, B.W.: Maintenance of Discovered Knowledge: A case in Multi-level Association Rules. In: Proceedings of ACM-KDD, pp. 307–310 (1996)
Cheung, D.W., Lee, S.D., Kao, B.: A general Incremental Technique for Mining Discovered Association Rules. In: Proceedings of DASFAA, pp. 185–194 (1997)
Koh, J.-L., Shieh, S.-F.: An Efficient Approach for Maintaining Association Rules Based on Adjusting FP-Tree Structures. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 417–424. Springer, Heidelberg (2004)
Das, Ng, W.K., Woon, Y.K.: Rapid Association Rule Mining. In: Proceedings of ACM-CIKM, pp. 474–481 (2001)
Ayan, N.F., Tansel, A.U., Arkun, E.: An Efficient Algorithm to Update Large Itemsets with Early Pruning. In: Proceedings of ACM SIGKDD (1999)
Feldman, R., Aumann, Y., Amir, A., Mannila, H.: Efficient Algorithms for Discovering Frequent Sets in Incremental Databases. In: Proceedings of the International Workshop Research Issues on Data Mining and Knowledge Discovery (1997)
Ganti, V., Gehrke, J.E., Ramakrishnan, R.: DEMON: Mining and Monitoring Evolving Data. IEEE TKDE 13(1), 50–63 (2001)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. of ACM-SIGMOD, Dallas, TX, pp. 1–12 (May 2000)
Thomas, S., Bodagala, S., Alsabti, K., Ranka, S.: An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases. In: Proceedings of ACM-SIGKDD, pp. 263–266 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Adnan, M., Alhajj, R., Barker, K. (2006). Constructing Complete FP-Tree for Incremental Mining of Frequent Patterns in Dynamic Databases. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_40
Download citation
DOI: https://doi.org/10.1007/11779568_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
Online ISBN: 978-3-540-35454-3
eBook Packages: Computer ScienceComputer Science (R0)