Abstract
Existing weighted frequent pattern (WFP) mining algorithms assume that each item has fixed weight. But in our real world scenarios the weight (price or significance) of an item can vary with time. Reflecting such change of weight of an item is very necessary in several mining applications such as retail market data analysis and web click stream analysis. In this paper, we introduce a novel concept of adaptive weight for each item and propose an algorithm AWFPM (adaptive weighted frequent pattern mining). Our algorithm can handle the situation where the weight (price or significance) of an item may vary with time. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using adaptive weights.
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
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)
Zhang, S., Zhang, C., Yan, X.: Post-mining: maintenance of association rules by weighting. Information Systems 28, 691–707 (2003)
Tao, F.: Weighted association rule mining using weighted support and significant framework. In: 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 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)
Jiang, N., Gruenwald, L.: Research Issues in Data Stream Association Rule Mining. SIGMOD Record 35(1), 14–19 (2006)
Leung, C.K.-S., Khan, Q.I.: DSTree: A Tree structure for the mining of frequent Sets from Data Streams. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 928–932. Springer, Heidelberg (2006)
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.: 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 Using Adaptive Weights. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-88906-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88905-2
Online ISBN: 978-3-540-88906-9
eBook Packages: Computer ScienceComputer Science (R0)