Abstract
By considering different weights of the items, weighted frequent pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery. However, existing algorithms cannot be applied for incremental and interactive WFP mining because they are based on a static database and require multiple database scans. In this paper, we present a novel tree structure \({\rm IWFPT}_{\textrm{\scriptsize{WA}}}\) (Incremental WFP tree based on weight ascending order) and an algorithm \({\rm IWFP}_{\textrm{\scriptsize{WA}}}\) for incremental and interactive WFP mining using a single database scan. Extensive performance analyses show that our tree structure and algorithm are efficient for incremental and interactive WFP mining.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th Int. Conf. on Very Large Data Bases (VLDB), pp. 487–499 (1994)
Yun, U., Leggett, J.J.: WFIM: weighted frequent itemset mining with a weight range and a minimum weight. In: Fourth SIAM Int. Conf. on Data Mining, USA, pp. 636–640 (2005)
Yun, U.: Efficient Mining of weighted interesting patterns with a strong weight and/or support affinity. Information Sciences 177, 3477–3499 (2007)
Tao, F.: Weighted association rule mining using weighted support and significant framework. In: 9th ACM SIGKDD, USA, pp. 661–666 (2003)
Wang, W., Yang, J., Yu, P.S.: WAR: weighted association rules for item intensities. Knowledge Information and Systems 6, 203–229 (2004)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery 8, 53–87 (2004)
Leung, C.K.-S., Khan, Q.I., Li, Z., Hoque, T.: CanTree: a canonical-order tree for incremental frequent-pattern mining. Knowledge and Information Systems 11(3), 287–311 (2007)
Tanbeer, S.K., Ahmed, C.F., Jeong, B.-S., Lee, Y.-K.: CP-tree: A tree structure for single-pass frequent pattern mining. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 1022–1027. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ahmed, C.F., Tanbeer, S.K., Jeong, BS., Lee, YK. (2008). Mining Weighted Frequent Patterns in Incremental Databases. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_87
Download citation
DOI: https://doi.org/10.1007/978-3-540-89197-0_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89196-3
Online ISBN: 978-3-540-89197-0
eBook Packages: Computer ScienceComputer Science (R0)