Abstract
In recent year, erasable itemset mining is an interesting problem in supply chain optimization problem. In the previous works, we presented dPidset structure, a very effective structure for mining erasable itemsets. The dPidset structure improves the preferment compared with the previous structures. However, the mining time is still large. Therefore, in this paper, we propose a new approach using the subsume concept for mining effectively erasable itemsets. The subsume concept helps early determine information of a large number of erasable itemsets without usual computational cost. The experiment was conducted to show the effectiveness of using subsume concept in the mining erasable itemsets process.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, pp. 487–499 (1994)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between set of items in large databases. In: SIGMOD 1993, pp. 207–216 (1993)
Deng, Z.H.: Mining top-rank-k erasable itemsets by PID_lists. International Journal of Intelligent Systems 28(4), 366–379 (2013)
Deng, Z.H., Xu, X.R.: Fast mining erasable itemsets using NC_sets. Expert Systems with Applications 39(4), 4453–4463 (2012)
Deng, Z., Fang, G., Wang, Z., Xu, X.: Mining erasable itemsets. In: ICMLC 2009, pp. 67–73 (2009)
Deng, Z., Xu, X.: An efficient algorithm for mining erasable itemsets. In: Cao, L., Feng, Y., Zhong, J. (eds.) ADMA 2010, Part I. LNCS, vol. 6440, pp. 214–225. Springer, Heidelberg (2010)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: SIGMOD 2000, pp. 1–12 (2003)
Le, T., Vo, B., Coenen, F.: An efficient algorithm for mining erasable itemsets using the difference of NC-Sets. In: IEEE SMC 2013, Manchester, UK, pp. 2270–2274 (2013)
Le, T., Vo, B.: MEI: an efficient algorithm for mining erasable itemsets. Engineering Applications of Artificial Intelligence 27, 155–166 (2014)
Nguyen, G., Le, T., Vo, B., Le, B.: A new approach for mining top-rank-k erasable itemsets. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) ACIIDS 2014, Part I. LNCS, vol. 8397, pp. 73–82. Springer, Heidelberg (2014)
Song, W., Yang, B., Xu, Z.: Index-BitTableFI: An improved algorithm for mining frequent itemsets. Knowledge-Based Systems 21, 507–513 (2008)
Van, T.-T., Vo, B., Le, B.: IMSR_PreTree: an improved algorithm for mining sequential rules based on the prefix-tree.  Vietnam J. Computer Science 1(2), 97–105 (2014)
Vo, B., Coenen, F., Le, T., Hong, T.-P.: A hybrid approach for mining frequent itemsets. In: IEEE SMC 2013, Manchester, UK, pp. 4647–4651 (2013)
Vo, B., Le, T., Coenen, F., Hong, T.-P.: Mining frequent itemsets using the N-list and subsume concepts. International Journal of Machine Learning and Cybernetics, http://dx.doi.org/10.1007/s13042-014-0252-2
Zaki, M.J.: Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering 12(3), 372–390 (2000)
Zaki, M.J., Gouda, K.: Fast vertical mining using diffsets. In: SIGKDD 2003, pp. 326–335 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Nguyen, G., Le, T., Vo, B., Le, B., Trinh, PC. (2014). Subsume Concept in Erasable Itemset Mining. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-11289-3_52
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11288-6
Online ISBN: 978-3-319-11289-3
eBook Packages: Computer ScienceComputer Science (R0)