Abstract
Since there are many real-life situations in which people are uncertain about the content of transactions, association rule mining with uncertain data is in demand. Most of these studies focus on the improvement of classical algorithms for frequent itemsets mining. To obtain a tradeoff between the accuracy and computation time, in this paper we introduces an efficient algorithm for finding association rules from uncertain data with sampling-SARMUT, which is based on the FAST algorithm introduced by Chen et al. Unlike FAST, SARMUT is designed for uncertain data mining. In response to the special characteristics of uncertainty, we propose a new definition of ”distance” as a measure to pick representative transactions. To evaluate its performance and accuracy, a comparison against the natural extension of FAST is performed using synthetic datasets. The experimental results show that the proposed sampling algorithm SARMUT outperforms FAST algorithm, and achieves up to 97% accuracy in some cases.
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.
Similar content being viewed by others
References
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)
Dai, X., Yiu, M.L., Mamoulis, N., Tao, Y., Vaitis, M.: Probabilistic spatial queries on existentially uncertain data. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 400–417. Springer, Heidelberg (2005)
Leung, C.K.-S., Mateo, M.A.F., Brajczuk, D.A.: A tree-based approach for frequent pattern mining from uncertain data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 653–661. Springer, Heidelberg (2008)
Leung, C.K.-S., Carmichael, C.L., Hao, B.: Efficient mining of frequent patterns from uncertain data. In: Proc. IEEE ICDM Workshops, pp. 489–494 (2007)
Chui, C.-K., Kao, B.: A decremental approach for mining frequent itemsets from uncertain data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 64–75. Springer, Heidelberg (2008)
Aggarwal, C.C., Li, Y., Wang, J.: Frequent pattern mining with uncertain data. In: Proc. KDD, pp. 29–37 (2009)
Brönnimann, H., Chen, B., Dash, M., Haas, P., Qiao, Y., Scheuerman, P.: Efficient data-reduction methods for online association rule discovery. In: Data Mining: Next Generation Challenges and Future Directions, pp. 190–208. AAAI Press, Menlo Park
Chen, B., Haas, P., Scheuermann, P.: A new two-phase sampling based algorithm for discovering association rule. In: Proc. 8th ACM SIGKDD, Edmonton, Alberta, Canada, July 23-26 (2002)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. VLDB, pp. 487–499 (1994)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. ACM SIGMOD, pp. 1–12 (2000)
Leung, C.K.-S., Brajczuk, D.A.: Efficient algorithms for mining constrained frequent patterns from uncertain data. In: Proc., pp. 9–18 (2009)
Zaki, M.J., Parthasarathy, S., Lin, W., Ogihara, M.: Evaluation of sampling for data mining of association rules. In: Proc. 3rd KDD, pp. 283–286 (1997)
Toivonen, H.: Sampling large databases for association rules. In: Proc. 22nd VLDB, pp. 134–145 (1996)
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
Qian, Z., Donghua, P., Guangfei, Y. (2010). A Sampling Based Algorithm for Finding Association Rules from Uncertain Data. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16530-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-16530-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16529-0
Online ISBN: 978-3-642-16530-6
eBook Packages: Computer ScienceComputer Science (R0)