Abstract
Positive correlation mining can find such type of patterns, “the conditional probability that a customer purchasing A is likely to also purchase B is not only great enough, but also significantly greater than the probability that a customer purchases only B.” However, there often exist many independence relationships between items in a correlated pattern due to the definition of a correlated pattern. Therefore, we mine mutually and positively correlated patterns, whose any two sub-patterns are both associated and positively correlated. A new correlation interestingness measure is proposed for rationally evaluating the correlation degree. In order to improve the mining efficiency, we combine association with correlation and use not only the correlation measure but also the association measure in the mining process. Our experimental results show that mutually and positively correlated pattern mining is a good approach to discovering patterns which can reflect both association and positive correlation relationships between items at the same time. Meanwhile, our experimental results show that the mining combined association with correlation is quite a valid method to decrease the execution time.
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References
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proc. 1993 ACM SIGMOD Int. Conf. Management of Data (SIGMOD 1993), pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proc. 1994 VLDB Int. Conf. Very Large Databases (VLDB 1994), pp. 487–499 (1994)
Liu, B., Hsu, W., Ma, Y.: Pruning and Summarizing the Discovered Association. In: Proc. 1999 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD 1999), pp. 15–18 (1999)
Brin, S., Motwani, R., Silverstein, C.: Beyond Market Basket: Generalizing Association Rules to Correlations. In: Proc. 1997 ACM SIGMOD Int. Conf. Management of Data (SIGMOD 1997), pp. 265–276 (1997)
Liu, H., Lu, H., Feng, L., Hussain, F.: Efficient Search of Reliable Exceptions. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 194–203. Springer, Heidelberg (1999)
Mills, F.: Statistical Methods. Pitman (1955)
Omiecinski, E.: Alternative interesting measures for mining associations. IEEE Trans. Knowledge and Data Engineering (15), 57–69 (2003)
Reynolds, H.T.: The Analysis of Cross-Classifications. The Free Press, New York (1977)
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhou, Z., Wu, Z., Wang, C., Feng, Y. (2006). Efficiently Mining Mutually and Positively Correlated 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_12
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DOI: https://doi.org/10.1007/11811305_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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