Abstract
Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic solutions. In the case of uncertain data, however, several new techniques have been proposed. Unfortunately, these proposals often suffer when a lot of items occur with many different probabilities. Here we propose an approach based on sampling by instantiating “possible worlds” of the uncertain data, on which we subsequently run optimized frequent itemset mining algorithms. As such we gain efficiency at a surprisingly low loss in accuracy. These is confirmed by a statistical and an empirical evaluation on real and synthetic data.
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
Aggarwal, C.C., Li, Y., Wang, J., Wang, J.: Frequent pattern mining with uncertain data. In: Proc. of KDD 2009, pp. 29–38. ACM, New York (2009)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. of VLDB 1994, pp. 487–499. Morgan Kaufmann Publishers Inc, San Francisco (1994)
Chui, C.K., Kao, B.: A decremental approach for mining frequent itemsets from uncertain data. In: Washio, et al [9], pp. 64–75
Chui, C.K., Kao, B., Hung, E.: Mining frequent itemsets from uncertain data. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 47–58. Springer, Heidelberg (2007)
Goethals, B.: Frequent set mining. In: The Data Mining and Knowledge Discovery Handbook, ch. 17, pp. 377–397. Springer, Heidelberg (2005)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)
Leung, C.K.-S., Mateo, M.A.F., Brajczuk, D.A.: A tree-based approach for frequent pattern mining from uncertain data. In: Washio, et al [9], pp. 653–661
Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-mine: Hyper-structure mining of frequent patterns in large databases. In: Proc. of ICDM 2001, Washington, DC, USA, pp. 441–448. IEEE Computer Society, Los Alamitos (2001)
Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.): PAKDD 2008. LNCS (LNAI), vol. 5012. Springer, Heidelberg (2008)
Zaki, M.J., Gouda, K.: Fast vertical mining using diffsets. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) Proc. of KDD 2003, pp. 326–335. ACM, New York (2003)
Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: Proc. of KDD 1997, pp. 283–286 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Calders, T., Garboni, C., Goethals, B. (2010). Efficient Pattern Mining of Uncertain Data with Sampling. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13657-3_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-13657-3_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13656-6
Online ISBN: 978-3-642-13657-3
eBook Packages: Computer ScienceComputer Science (R0)