Abstract
Over the past two decades, numerous algorithms have been proposed for mining frequent itemsets from precise data. However, there are situations in which data are uncertain. In recent years, tree-based algorithms have been proposed to mine frequent itemsets from uncertain data. While the key success of tree-based algorithms for mining precise data is due to the compactness of a tree structure in capturing precise data, the corresponding tree structure in capturing uncertain data may not be so compact. In this paper, we propose a novel tree structure for capturing uncertain data such that it is as compact as the tree for capturing precise data. Moreover, we also propose two fast algorithms that use this compact tree structure to mine frequent itemsets. Experimental results showed the compactness of our tree and the effectiveness of our algorithms in mining frequent itemsets from uncertain data.
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References
Aggarwal, C.C., Li, Y., Wang, J., Wang, J.: Frequent pattern mining with uncertain data. In: ACM KDD 2009, pp. 29–37 (2009)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, pp. 487–499 (1994)
Bernecker, T., Kriegel, H.-P., Renz, M., Verhein, F., Zuefle, A.: Probabilistic frequent itemset mining in uncertain databases. In: ACM KDD 2009, pp. 119–127 (2009)
Calders, T., Garboni, C., Goethals, B.: Approximation of frequentness probability of itemsets in uncertain data. In: IEEE ICDM 2010, pp. 749–754 (2010)
Calders, T., Garboni, C., Goethals, B.: Efficient Pattern Mining of Uncertain Data with Sampling. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010, Part I. LNCS (LNAI), vol. 6118, pp. 480–487. Springer, Heidelberg (2010)
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)
Eavis, T., Zheng, X.: Multi-Level Frequent Pattern Mining. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds.) DASFAA 2009. LNCS, vol. 5463, pp. 369–383. Springer, Heidelberg (2009)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD 2000, pp. 1–12 (2000)
Kiran, R.U., Reddy, P.K.: An Alternative Interestingness Measure for Mining Periodic-Frequent Patterns. In: Yu, J.X., Kim, M.H., Unland, R. (eds.) DASFAA 2011, Part I. LNCS, vol. 6587, pp. 183–192. Springer, Heidelberg (2011)
Lakshmanan, L.V.S., Leung, C.K.-S., Ng, R.T.: Efficient dynamic mining of constrained frequent sets. ACM TODS 28(4), 337–389 (2003)
Leung, C.K.-S., Hao, B.: Mining of frequent itemsets from streams of uncertain data. In: IEEE ICDE 2009, pp. 1663–1670 (2009)
Leung, C.K.-S., Jiang, F.: Frequent Pattern Mining from Time-Fading Streams of Uncertain Data. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 252–264. Springer, Heidelberg (2011)
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)
Lin, C.-W., Hong, T.-P., Lu, W.-H.: A new tree structure for mining frequent itemsets from uncertain databases. In: Nat’l Conf. on Fuzzy Theory and its App., pp. 575–579 (2009)
Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-Mine: hyper-structure mining of frequent patterns in large databases. In: IEEE ICDM 2001, pp. 441–448 (2001)
Zhang, Q., Li, F., Yi, K.: Finding frequent items in probabilistic data. In: ACM SIGMOD 2008, pp. 819–832 (2008)
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Leung, C.KS., Tanbeer, S.K. (2012). Fast Tree-Based Mining of Frequent Itemsets from Uncertain Data. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29038-1_21
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DOI: https://doi.org/10.1007/978-3-642-29038-1_21
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