Abstract
Mutually associated pattern mining can find such type of patterns whose any two sub-patterns are associated. However, like frequent pattern mining, when the minimum association threshold is set to be low, it still generates a large number of mutually associated patterns. The huge number of patterns produced not only reduces the mining efficiency, but also makes it very difficult for a human user to analyze in order to identify interesting/useful ones. In this paper, a new task of maximal frequent mutually associated pattern mining is proposed, which can dramatically decrease the number of patterns produced without information loss due to the downward closure property of the association measure and meanwhile improve the mining efficiency. Experimental results show that maximal frequent mutually associated pattern mining is quite a necessary approach to lessening the number of results and increasing the performance of the algorithm. Also, experimental results show that the techniques developed are much effective especially for very large and dense datasets.
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References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proc. 1994 Int. Conf. Very Large Databases, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. 1995 Int. Conf. Data Engineering, pp. 3–14 (1995)
Han, J., Dong, G., Yin, Y.: Efficient mining of partial periodic patterns in time series database. In: Proc.1999 Int. conf. Data Engineering, pp. 106–115 (1999)
Mannila, H., Toivonen, H., Verkamo, A.I.: Efficient algorithms for discovering association rules. In: Proc. AAAI 1994 Workshop Knowledge Discovery in Databases, pp. 181–192 (1994)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discover of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1, 259–289 (1997)
Omiecinski, E.: Alternative interesting measures for mining associations. IEEE Trans. Knowledge and Data Engineering 15, 57–69 (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)
Lee, Y.-K., Kim, W.-Y., Cai, Y.D., Han, J.: CoMine: Efficient Mining of Correlated Patterns. In: Proc. 2003 Int. Conf. Data Mining, pp. 581–584 (2003)
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Zhou, Z., Wu, Z., Wang, C., Feng, Y. (2006). Efficiently Mining Maximal Frequent Mutually Associated Patterns. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_11
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DOI: https://doi.org/10.1007/11811305_11
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