Abstract
Utility mining has been emerging as an important area in data mining. While existing works on utility mining have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this paper considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three-fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be filled. Second, we propose a novel flexible and efficient index, designated as the STUI index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots. Third, we conducted an extensive performance evaluation using real datasets to demonstrate the overall effectiveness of the proposed indexing scheme in terms of execution time and utility (net revenue) as compared to a recent existing scheme.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hansen, P., Heinsbroek, H.: Product selection and space allocation in supermarkets. Eur. J. Oper. Res. 3, 474–484 (1979)
Yang, M.H., Chen, W.C.: A study on shelf space allocation and management. Int. J. Prod. Econ. 60–61, 309–317 (1999)
Yang, M.H.: An efficient algorithm to allocate shelf space. Eur. J. Oper. Res. 131, 107–118 (2001)
Chen, M.C., Lin, C.P.: A data mining approach to product assortment and shelf space allocation. Expert Syst. Appl. 32, 976–986 (2007)
Chen, Y.L., Chen, J.M., Tung, C.W.: A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales. Decis. Support Syst. 42, 1503–1520 (2006)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: 20th International Conference, VLDB, pp. 487–499 (1994)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM Sigmod Record 29, 1–12 (2000)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Beeri, C., Buneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-49257-7_25
World’s largest retail store. https://www.thebalance.com/largest-retail-stores-2892923
US Retail Industry. https://www.thebalance.com/us-retail-industry-overview-2892699
Fournier-Viger, P., Wu, C.-W., Tseng, V.S.: Novel concise representations of high utility itemsets using generator patterns. In: Luo, X., Yu, J.X., Li, Z. (eds.) ADMA 2014. LNCS (LNAI), vol. 8933, pp. 30–43. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-14717-8_3
Fournier-Viger, P., Lin, J.C.-W., Wu, C.-W., Tseng, V.S., Faghihi, U.: Mining minimal high-utility itemsets. In: Hartmann, S., Ma, H. (eds.) DEXA 2016. LNCS, vol. 9827, pp. 88–101. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44403-1_6
Zida, S., Fournier-Viger, P., Lin, J.C.-W., Wu, C.-W., Tseng, V.S.: EFIM: a highly efficient algorithm for high-utility itemset mining. In: Sidorov, G., Galicia-Haro, S.N. (eds.) MICAI 2015. LNCS (LNAI), vol. 9413, pp. 530–546. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27060-9_44
Fournier-Viger, P., Zida, S., Lin, J.C.-W., Wu, C.-W., Tseng, V.S.: EFIM-Closed: fast and memory efficient discovery of closed high-utility itemsets. In: Perner, P. (ed.) Machine Learning and Data Mining in Pattern Recognition. LNCS (LNAI), vol. 9729, pp. 199–213. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41920-6_15
Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 55–64. ACM (2012)
Liu, Y., Liao, W.K., Choudhary, A.: A fast high utility itemsets mining algorithm. In: Proceedings of the 1st International Workshop on Utility-Based Data Mining, pp. 90–99 (2005)
Tseng, V.S., Wu, C.W., Shie, B.E., Yu, P.S.: UP-Growth: an efficient algorithm for high utility itemset mining. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 253–262. ACM (2010)
Tseng, V.S., Wu, C.W., Fournier-Viger, P., Philip, S.Y.: Efficient algorithms for mining the concise and lossless representation of high utility itemsets. IEEE Trans. Knowl. Data Eng. 27, 726–739 (2015)
Chan, R., Yang, Q., Shen, Y.D.: Mining high utility itemsets. In: 3rd IEEE International Conference on Data Mining, ICDM, pp. 19–26 (2003)
SPMF (Open-source data mining library). http://www.philippe-fournier-viger.com/spmf/dataset
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 (LNAI), vol. 8502, pp. 83–92. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08326-1_9
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chaudhary, P., Mondal, A., Reddy, P.K. (2017). A Flexible and Efficient Indexing Scheme for Placement of Top-Utility Itemsets for Different Slot Sizes. In: Reddy, P., Sureka, A., Chakravarthy, S., Bhalla, S. (eds) Big Data Analytics. BDA 2017. Lecture Notes in Computer Science(), vol 10721. Springer, Cham. https://doi.org/10.1007/978-3-319-72413-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-72413-3_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72412-6
Online ISBN: 978-3-319-72413-3
eBook Packages: Computer ScienceComputer Science (R0)