Abstract
The quest for frequent itemsets in a transactional database is explored in this paper, for the purpose of extracting hidden patterns from the database. Two major limitations of the Apriori algorithm are tackled, (i) the scan of the entire database at each pass to calculate the support of all generated itemsets, and (ii) its high sensitivity to variations of the minimum support threshold defined by the user. To deal with these limitations, a novel approach is proposed in this paper. The proposed approach, called Single Scan Frequent Itemsets Mining (SS-FIM), requires a single scan of the transactional database to extract the frequent itemsets. It has a unique feature to allow the generation of a fixed number of candidate itemsets, independently from the minimum support threshold, which intuitively allows to reduce the cost in terms of runtime for large databases. SS-FIM is compared with Apriori using several standard databases. The results confirm the scalability of SS-FIM and clearly show its superiority compared to Apriori for medium and large databases.
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
Similar content being viewed by others
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, no. 2, pp. 207–216. ACM, June 1993
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD Record, vol. 29, no. 2, pp. 1–12. ACM, May 2000
Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: ACM SIGMOD Record, vol. 26, no. 2, pp. 255–264. ACM, June 1997
Mueller, A.: Fast sequential and parallel algorithms for association rule mining: a comparison. Technical report CS-TR-3515, University of Maryland, College Park, August 1995
Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: Third International Conference Knowledge Discovery and Data Mining (1997)
Amphawan, K., Lenca, P., Surarerks, A.: Efficient mining top-k regular-frequent itemset using compressed tidsets. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds.) PAKDD 2011. LNCS (LNAI), vol. 7104, pp. 124–135. Springer, Heidelberg (2012). doi:10.1007/978-3-642-28320-8_11
Leung, C.K.-S., Mateo, M.A.F., Brajczuk, D.A.: A tree-based approach for frequent pattern mining from uncertain data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 653–661. Springer, Heidelberg (2008). doi:10.1007/978-3-540-68125-0_61
Cerf, L., Besson, J., Robardet, C., Boulicaut, J.F.: Closed patterns meet n-ary relations. ACM Trans. Knowl. Discov. Data (TKDD) 3(1), 3 (2009)
Grahne, G., Zhu, J.: Fast algorithms for frequent itemset mining using FP-trees. IEEE Trans. Knowl. Data Eng. 17(10), 1347–1362 (2005)
Borgelt, C.: Frequent itemset mining. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 2(6), 437–456 (2012)
Djenouri, Y., Bendjoudi, A., Mehdi, M., Nouali-Taboudjemat, N., Habbas, Z.: GPU-based bees swarm optimization for association rules mining. J. Supercomput. 71(4), 1318–1344 (2015)
Djenouri, Y., Drias, H., Habbas, Z.: Bees swarm optimisation using multiple strategies for association rule mining. Int. J. Bio-Inspired Comput. 6(4), 239–249 (2014)
Gheraibia, Y., Moussaoui, A., Djenouri, Y., Kabir, S., Yin, P.Y.: Penguins search optimisation algorithm for association rules mining. CIT J. Comput. Inf. Technol. 24(2), 165–179 (2016)
Luna, J.M., Pechenizkiy, M., Ventura, S.: Mining exceptional relationships with grammar-guided genetic programming. Knowl. Inf. Syst. 47(3), 571–594 (2016)
Hegland, M.: The apriori algorithm tutorial. Math. Comput. imaging Sci. Inf. Process. 11, 209–262 (2005)
Guvenir, H.A., Uysal, I.: Bilkent university function approximation repository (2000). http://funapp.CS.bilkent.edu.tr/DataSets. Accessed 12 Mar 2012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Djenouri, Y., Comuzzi, M., Djenouri, D. (2017). SS-FIM: Single Scan for Frequent Itemsets Mining in Transactional Databases. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-57529-2_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57528-5
Online ISBN: 978-3-319-57529-2
eBook Packages: Computer ScienceComputer Science (R0)