Abstract
Recently, many approaches for high utility pattern mining (HUPM) have been proposed, but most of them aim at mining high-utility patterns (HUPs) instead of frequent ones. The major drawback is that any combination of a low-utility item with a very high utility pattern is regarded as a HUP, even if this combination is infrequent and contains items that rarely co-occur. Thus, the HUIPM algorithm was proposed to derive high utility interesting patterns (HUIPs) with strong frequency affinity. However, it recursively constructs a series of conditional trees to produce candidates, and then derive the HUIPs. It is time-consuming and may lead to a combinatorial explosion. In this paper, a Fast algorithm for mining Discriminative High Utility Patterns with strong frequency affinity (FDHUP) is proposed by considering both the utility and frequency affinity constraints. Two compact structures named EI-table and FU-table, and two pruning strategies are designed to reduce the search space, and efficiently and effectively discover DHUPs. Experimental results show that the proposed FDHUP algorithm considerably outperforms the state-of-the-art HUIPM algorithm in all datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Frequent itemset mining dataset repository. http://fimi.ua.ac.be/data/
Agrawal, R., Imielinski, T., Swami, A.: Database mining: a performance perspective. IEEE Trans. Knowl. Data Eng. 5(6), 914–925 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The Intentional Conference on Very Large Data Bases, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Quest synthetic data generator. http://www.Almaden.ibm.com/cs/quest/syndata.html
Ahmed, C.F., Tanbeer, S.K., Jeong, B.S., Le, H.J.: Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans. Knowl. Data Eng. 21(12), 1708–1721 (2009)
Ahmed, C.F., Tanbeer, S.K., Jeong, B.S., Choi, Y.K.: A framework for mining interesting high utility patterns with a strong frequency affinity. Inf. Sci. 181(21), 4878–4894 (2011)
Chan, R., Yang, Q., Shen, D.: Minging high utility itemsets. In: IEEE International Conference on Data Mining, pp. 19–26 (2003)
Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6), 866–883 (1996)
Fournier-Viger, P., Wu, C.-W., Zida, S., Tseng, V.S.: FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning. In: Andreasen, T., Christiansen, H., Cubero, J.-C., Raś, Z.W. (eds.) ISMIS 2014. LNCS, vol. 8502, pp. 83–92. Springer, Heidelberg (2014)
Lin, J.C.-W., Gan, W., Hong, T.-P., Pan, J.-S.: Incrementally updating high-utility itemsets with transaction insertion. In: Luo, X., Yu, J.X., Li, Z. (eds.) ADMA 2014. LNCS, vol. 8933, pp. 44–56. Springer, Heidelberg (2014)
Lin, J.C.W., Gan, W., Fournier-Viger, P., Hong, T.P.: Mining high-utility itemsets with multiple minimum utility thresholds. In: The International C* Conference on Computer Science and Software Engineering, pp. 9–17 (2015)
Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: ACM International Conference on Information and Knowledge Management, pp. 55–64 (2012)
Liu, Y., Liao, W., Choudhary, A.K.: A two-phase algorithm for fast discovery of high utility itemsets. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 689–695. Springer, Heidelberg (2005)
Rymon, R.: Search through systematic set enumeration. Technical reports, 297 (1992)
Tseng, V.S., Wu, C.W., Shie, B.E., Yu, P.S.: UP-Growth: an efficient algorithm for high utility itemset mining. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 253–262 (2010)
Tseng, V.S., Wu, C.W., Shie, B.E., Yu, P.S.: Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans. Knowl. Data Eng. 25(8), 1772–1786 (2013)
Yao, H., Hamilton, H.J., Butz, C.J.: A foundational approach to mining itemset utilities from databases. In: SIAM International Conference on Data Mining, pp. 211–225 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, J.CW., Gan, W., Fournier-Viger, P., Hong, TP. (2016). Mining Discriminative High Utility Patterns. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-662-49390-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49389-2
Online ISBN: 978-3-662-49390-8
eBook Packages: Computer ScienceComputer Science (R0)