Abstract
One of the most important tasks in data mining is to discover associations and correlations among items in a huge database. In recent years, some studies have been conducted to find a more accurate measure to describe correlations between items. It has been proved that the newly developed measures of all-confidence and bond perform much better in reflecting the true correlation relationship than just using support and confidence in categorical database. Hence, several efficient algorithms have been proposed to mine correlated patterns based on all-confidence and bond. However, as the data uncertainty become increasingly prevalent in various kinds of real-world applications, we need a brand new method to mine the true correlations in uncertain datasets with high efficiency and accuracy. In this paper, we propose effective methods based on dynamic programming to compute the expected all-confidence and expected bond, which could serve as a slant in finding correlated patterns in uncertain datasets.
This work is supported in part by the National Natural Science Foundation of China under grant 60703012; the Key Program of National Natural Science Foundation of China under grant 60933001.
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
Gao, C., Wang, J.: Direct Mining of Discriminative Patterns for Classifying Uncertain Data. In: Proceedings of 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, pp. 861–870 (2010)
Bernecker, T., Riegel, H., Renz, M., Verhein, F., Zuefle, A.: Probabilistic Frequent Itemset Mining in Uncertain Databases. In: Proceedings of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, pp. 119–128 (2009)
Omiecinski, E.R.: Alternative Interest Measures for Mining Associations in Databases. IEEE Transactions on Knowledge and Data Engineering, TKDE 15, 57–69 (2003)
Lee, Y.K., Kim, W.Y., Cai, Y.D., Han, J.: CoMine: Efficient Mining of Correlated Patterns. In: Proceedings of 3rd IEEE International Conference on Data Mining, ICDM 2003, Melbourne, FL, USA, pp. 581–584 (2003)
Kim, W.-Y., Lee, Y.-K., Han, J.: CcMine: Efficient Mining of Confidence-Closed Correlated Patterns. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 569–579. Springer, Heidelberg (2004)
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)
Leung, C.K.S., Carmichael, C.L., Hao, B.: Efficient Mining of Frequent Patterns from Uncertain Data. In: Workshops Proceedings of the 7th IEEE International Conference on Data Mining, PAKDD 2007, Omaha, Nebraska, USA, pp. 489–494 (2007)
Aggarwal, C.C., Li, Y., Wang, J., Wang, J.: Frequent pattern mining with uncertain data. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, pp. 29–38 (2009)
Wang, J., Karypis, G.: On mining instance-centric classification rules. IEEE Transactions on Knowledge and Data Engineering, TKDE 18, 1497–1511 (2006)
Qin, B., Xia, Y., Li, F.: DTU: A Decision Tree for Uncertain Data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 4–15. Springer, Heidelberg (2009)
Qin, B., Xia, Y., Prabhakar, S., Tu, Y.-C.: A rule-based classification algorithm for uncertain data. In: Proceedings of the IEEE 25th International Conference on Data Engineering, ICDE 2009, Shanghai, China, pp. 1633–1640 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, L., Shi, S., Lv, J. (2011). Efficient Computation of Measurements of Correlated Patterns in Uncertain Data. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25853-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-25853-4_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25852-7
Online ISBN: 978-3-642-25853-4
eBook Packages: Computer ScienceComputer Science (R0)