Abstract
Almost every global economy has now entered the digital era. Most stores are trading online and they are highly competitive. Traditional association rule mining is not suitable for online trading because information is dynamic and it needs a fast processing time. Sometimes products on online stores are out of stock or unavailable which needs to be manually addressed. Therefore, this work proposes a new algorithm called Association Rule Mining by Frequency-Edge-Graph (ARMFEG) that can convert transaction data to form a complete virtual graph and store items counting in the adjacency matrix. With the limitation of search space using the top weight which is automatically generated during frequency items generation, ARMFEG is very fast during the rule generation phase and can find association rules in all items from adjacency matrix which solves the rare item problem.
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
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann Publishers Inc., CA (1994)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 1–12. ACM, Texas (2000)
Narvekar, M., Syed, S.F.: Anoptimized algorithm for association rule mining using FP tree. Procedia Comput. Sci. 45, 101–110 (2015)
Dong, J., Han, M.: BitTableFI: an efficient mining frequent itemsets algorithm. Knowl. Based Syst. 20, 329–335 (2007)
Asadi, A., Afzali, M., Shojaei, A., Sulaimani, S.: New binary PSO based method for finding best thresholds in association rule mining. Life Sci. J. 9, 260–264 (2012)
Pears, R., Koh, Y.S., Dobbie, G., Yeap, W.: Weighted association rule mining via a graph based connectivity model. Inf. Sci. 218, 61–84 (2013)
Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: The Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, California, pp. 337–341 (1999)
Duck, J., Long, J., Hwang, B., Keun-Ho, R.: Frequent pattern mining using bipartite graph. In: Proceedings of 18th International Workshop on Database and Expert Systems Applications, DEXA 2007, pp. 182–186 (2007)
Tiwari, V., Tiwari, V., Gupta, S., Tiwari, R.: Association rule mining: A graph based approach for mining frequent itemsets. In: Proceedings of International Conference on Networking and Information Technology (ICNIT), pp. 309–313 (2010)
Al-Dharhani, G., Othman, Z., Bakar, A.: A graph-based ant colony optimization for association rule mining. Arab. J. Sci. Eng. 39, 4651–4665 (2014)
Pears, R., Pisalpanus, S., Koh, Y.S.: A graph based approach to inferring item weights for pattern mining. Expert Syst. Appl. 42, 451–461 (2015)
Amal Dev, P., Shobana, N.V., Joseph, P.: GARM: A simple graph based algorithm for association rule mining. Int. J. Comput. Appl. 76(16) ,1–4 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Suksakaophong, P., Meesad, P., Unger, H. (2019). ARMFEG: Association Rule Mining by Frequency-Edge-Graph for Rare Items. In: Unger, H., Sodsee, S., Meesad, P. (eds) Recent Advances in Information and Communication Technology 2018. IC2IT 2018. Advances in Intelligent Systems and Computing, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-93692-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-93692-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93691-8
Online ISBN: 978-3-319-93692-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)