Abstract
High utility itemset mining is an important model with many real-world applications. But the popular adoption and successful industrial application of this model has been hindered by the following two limitations: (i) computational expensiveness of the model and (ii) infrequent itemsets may be output as high utility itemsets. This paper makes an effort to address these two limitations. A generic high utility-frequent itemset model is introduced to find all itemsets in the data that satisfy user-specified minimum support and minimum utility constraints. Two new pruning measures, named cutoff utility and suffix utility, are introduced to reduce the computational cost of finding the desired itemsets. A single phase fast algorithm, called High Utility Frequent Itemset Miner (HU-FIMi), is introduced to discover the itemsets efficiently. Experimental results demonstrate that the proposed algorithm is efficient.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
More details of this dataset are presented in latter parts of this paper.
- 2.
Since the local utility measure generalizes the TWU measure by taking into account itemsets, we use the former measure throughout this paper for brevity.
References
Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.W., Tseng, V.S.: SPMF: a Java open-source pattern mining library. J. Mach. Learn. Res. 15(1), 3389–3393 (2014)
Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Hong, T.P., Fujita, H.: A survey of incremental high-utility itemset mining. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 8(2), e1242 (2018)
Liu, J., Wang, K., Fung, B.C.: Direct discovery of high utility itemsets without candidate generation. In: ICDM, pp. 984–989. IEEE (2012)
Liu, Y., Liao, W., Choudhary, A.: 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). https://doi.org/10.1007/11430919_79
Pei, J., Han, J., Wang, W.: Constraint-based sequential pattern mining: the pattern-growth methods. J. Intell. Inf. Syst. 28(2), 133–160 (2007)
Tseng, V.S., Shie, B.E., Wu, C.W., 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, pp. 482–486 (2004)
Zhang, C., Almpanidis, G., Wang, W., Liu, C.: An empirical evaluation of high utility itemset mining algorithms. Expert Syst. with Appl. 101, 91–115 (2018)
Zida, S., Fournier-Viger, P., Lin, J.C.W., Wu, C.W., Tseng, V.S.: EFIM: a fast and memory efficient algorithm for high-utility itemset mining. Knowl. Inf. Syst. 51(2), 595–625 (2017)
Acknowledgements
We would like to thank Yahoo Japan Corporation for providing the retail transaction data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Uday Kiran, R., Yashwanth Reddy, T., Fournier-Viger, P., Toyoda, M., Krishna Reddy, P., Kitsuregawa, M. (2019). Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11440. Springer, Cham. https://doi.org/10.1007/978-3-030-16145-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-16145-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16144-6
Online ISBN: 978-3-030-16145-3
eBook Packages: Computer ScienceComputer Science (R0)