Finding frequent items: novel method for improving Apriori algorithm

• Autorzy: Karimtabar Noorollah , Fard Mohammad Javad Shayegan

Identyfikatory

DOI

10.7494/csci.2022.23.2.3776

• Języki publikacji: EN

Abstrakty

EN

In this paper, we use an intelligent method for improving the Apriori algorithm in order to extract frequent itemsets. PAA (the proposed Apriori algorithm) pursues two goals: first, it is not necessary to take only one data item at each step – in fact, all possible combinations of items can be generated at each step; and second, we can scan only some transactions instead of scanning all of the transactions to obtain a frequent itemset. For performance evaluation, we conducted three experiments with the traditional Apriori, BitTableFI, TDM-MFI, and MDC-Apriori algorithms. The results exhibited that the algorithm execution time was significantly reduced due to the significant reduction in the number of transaction scans to obtain the itemset. As in the first experiment, the time that was spent to generate frequent items underwent a reduction of 52% as compared to the algorithm in the first experiment. In the second experiment, the amount of time that was spent was equal to 65%, while in the third experiment, it was equal to 46%.

Słowa kluczowe

EN

apriori algorithm; frequent itemset; intelligent method

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2022
• Tom: T. 23 (2)
• Strony: 161--177
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Karimtabar Noorollah

• The University of Isfahan, Faculty of Computer Engineering, Isfahan, Iran

autor

Fard Mohammad Javad Shayegan

• The University of Science and Culture, Department of Computer Engineering, Tehran, Iran

Bibliografia

• [1] Agrawal R., Imieli´nski T., Swami A.: Mining association rules between sets of items in large databases. In: ACM-SIGMOD International Conference Management of Data, pp. 207–216, 1993.
• [2] Ai D., Pan H., Li X., Gao Y., He D.: Association rule mining algorithms on highdimensional datasets, Artificial Life and Robotics, vol. 23, pp. 420–427, 2018. doi: 10.1007/s10015-018-0437-y.
• [3] Benhamouda N.C., Drias H., Hir`eche C.: Meta-Apriori: A New Algorithm for Frequent Pattern Detection. In: N.T. Nguyen, B. Trawi´nski, H. Fujita, T.P. Hong (eds.), ACIIDS 2016: Intelligent Information and Database Systems, pp. 277–285, Springer, Berlin–Heidelberg, 2016.
• [4] Bhalodiya D., Patel K.M., Patel C.: An efficient way to find frequent pattern with dynamic programming approach. In: 2013 Nirma University International Conference on Engineering (NUiCONE), pp. 1–5, 2013. doi: 10.1109/NUiCONE. 2013.6780102.
• [5] Bhandari A., Gupta A., Das D.: Improvised Apriori algorithm using frequent pattern tree for real time applications in data mining. In: International Conference on Information and Communication Technologies, 2014.
• [6] Cheng X., Su S., Xu S., Li Z.: DP-Apriori: A differentially private frequent itemset mining algorithm based on transaction splitting, Computers & Security, vol. 50, pp. 74–90, 2015. doi: 10.1016/j.cose.2014.12.005.
• [7] Dong J., Han M.: BitTableFI: An efficient mining frequent itemsets algorithm, Knowledge-Based Systems, vol. 20, pp. 329–335, 2007. doi: 10.1016/j.knosys.2006.08.005.
• [8] Duong H.V., Truong T.C.: An efficient method for mining association rules based on minimum single constraint, Vietnam Journal of Computer Science, vol. 2, pp. 67–83, 2015. doi: 10.1007/s40595-014-0032-7.
• [9] Han J., Kamber M.: Data Mining Concepts and Techniquesr, Morgan Kaufmann Publishers, 2006.
• [10] Jie Z., Gang W.: Intelligence Data Mining Based on Improved Apriori Algorithm, Journal of Computers, vol. 14(1), pp. 52–62, 2019. doi: 10.17706/jcp.14.1.52-62.
• [11] Liu X., Zhai K., Pedrycz W.: An improved association rules mining method, Expert Systems with Applications, vol. 39(1), pp. 1362–1374, 2012. doi: 10.1016/j.eswa.2011.08.018.
• [12] Liu Y., Li Y., Yang J., Ren Y., Sun G., Li Q.: An Improved Apriori Algorithm Based on Matrix and Double Correlation Profit Constraint. In: Q. Zhou, Y. Gan, W. Jing, X. Song, Y. Wang, Z. Lu (eds.), ICPCSEE 2018: Data Science. Communications in Computer and Information Science, vol. 901, pp. 359–370, Springer, Singapore, 2018. doi: 10.1007/978-981-13-2203-7 27.
• [13] Sun L.: An improved Apriori algorithm based on support weight matrix for data mining in transaction database, Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp. 495–501, 2020. doi: 10.1007/s12652-019-01222-4.
• [14] Yabing J.: Research of an Improved Apriori Algorithm in Data Mining Association Rules, Journal of Computer and Communication Engineering, vol. 2(1), pp. 25–27, 2013.
• [15] Yu H., Wen J., Wang H., Jun L.: An Improved Apriori Algorithm Based On the Boolean Matrix and Hadoop, Procedia Engineering, vol. 15, pp. 1827–1831, 2011. doi: 10.1016/j.proeng.2011.08.340.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).