Abstract
Correlated patterns are an important class of regularities that exist in a transactional database. CoMine uses pattern-growth technique to discover the complete set of correlated patterns that satisfy the user-defined minimum support and minimum all-confidence constraints. The technique involves compacting the database into FP-tree, and mining it recursively by building conditional pattern bases (CPB) for each item (or suffix pattern) in FP-tree. The CPB of the suffix pattern in CoMine represents the set of complete prefix paths in FP-tree co-occurring with itself. Thus, CoMine implicitly assumes that the suffix pattern can concatenate with all items in its prefix paths to generate correlated patterns of higher-order. It has been observed that such an assumption can cause performance problems in CoMine. This paper makes an effort to improve the performance of CoMine by introducing a novel concept known as items’ support intervals. The concept says that an item in FP-tree can generate correlated patterns of higher-order by concatenating with only those items in its prefix-paths that have supports within a specific interval. We call the proposed algorithm as CoMine++. Experimental results on various datasets show that CoMine++ can discover high correlated patterns effectively.
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References
Frequent itemset mining repository, http://fimi.cs.helsinki.fi/data/
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD 1993, pp. 207–216. ACM (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, pp. 487–499 (1994)
Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: generalizing association rules to correlations. SIGMOD Rec. 26(2), 265–276 (1997)
Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery 14(1) (2007)
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)
Kim, S., Barsky, M., Han, J.: Efficient Mining of Top Correlated Patterns Based on Null-Invariant Measures. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 177–192. Springer, Heidelberg (2011)
Lee, Y.K., Kim, W.Y., Cai, D., Han, J.: Comine: efficient mining of correlated patterns, pp. 581–584 (November 2003)
Omiecinski, E.R.: Alternative interest measures for mining associations in databases. IEEE Trans. on Knowl. and Data Eng. 15(1), 57–69 (2003)
Sim, K., Phua, C., Yap, G., Biswas, J., Mokhtari, M.: Activity recognition using correlated pattern mining for people with dementia. In: Conf. Proc. IEEE Eng. Med. Biol. Soc. (2011)
Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: KDD 2002, pp. 32–41. ACM, New York (2002)
Wu, T., Chen, Y., Han, J.: Re-examination of interestingness measures in pattern mining: a unified framework. Data Min. Knowl. Discov. 21(3), 371–397 (2010)
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)
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Uday Kiran, R., Kitsuregawa, M. (2012). Efficient Discovery of Correlated Patterns in Transactional Databases Using Items’ Support Intervals. In: Liddle, S.W., Schewe, KD., Tjoa, A.M., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2012. Lecture Notes in Computer Science, vol 7446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32600-4_18
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DOI: https://doi.org/10.1007/978-3-642-32600-4_18
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