Abstract
We study the problem of mining correlated patterns. Correlated patterns have advantages over associations that they cover not only frequent items, but also rare items.Tight correlated item sets is a concise representation of correlated patterns, where items are correlated each other. Although finding such tight correlated item sets is helpful for applications, the algorithm’s efficiency is critical, especially for high dimensional database. Thus, we first prove Lemma 1 and Lemma 2 in theory. Utilizing Lemma 1 and Lemma 2, we design an optimized RSC (Regional-Searching-Correlations) algorithm. Furthermore, we estimate the amount of pruned search space for data with various support distributions based on a probabilistic model. Experiment results demonstrate that RSC algorithm is much faster than other similar algorithms.
This work was supported by the National Natural Science Foundation of China under Grant No. 60473072, Grant No. 60473051, and the National High Technology Research and Development Program of China (863 Program) 2006AA01Z230.
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© 2007 Springer Berlin Heidelberg
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Jiang, L., Yang, D., Tang, S., Ma, X., Zhang, D. (2007). Tight Correlated Item Sets and Their Efficient Discovery. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_11
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DOI: https://doi.org/10.1007/978-3-540-72524-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72483-4
Online ISBN: 978-3-540-72524-4
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