Finding Frequent Items: A Novel Method for Improving the Apriori Algorithm
Keywords:Apriori algorithm, Frequent itemset, Intelligent method,
In the current paper, we use an intelligent method for improved the Apriori algorithm in order to extract frequent itemsets. PAA (proposed Apriori algorithm) is twofold. First, it is not necessary to take only one data item at each step. In fact, all possible combinations of the items could be generated at each step. Secondly, we can scan only some transactions instead of scanning all the transactions to obtain frequent itemset. For performance evaluation, we conducted three experiments with the traditional Apriori, BitTableFI, TDM-MFI, and MDC_Apriori algorithms. The results exhibit that due to the significant reduction in the number of transaction scans to obtain the itemset, the algorithm execution time is significantly reduced; as in the first experiment, the time spent to generate frequent items underwent a reduction by 52% compared to the algorithm in the first experiment. In the second experiment, the amount of time spent is equal to 65%, while in the third experiment, it is equal to 46%.
Han J, Kamber M. (2006) Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco.
Yu H, Wenb J, Wangc H. (2011) An Improved Apriori Algorithm Based On the Boolean Matrix and Hadoop, Procedia Engineering, 15:1827-1831, https://doi.org/10.1016/j.proeng.2011.08.340.
Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases, ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’93), pp. 207–216.
Chai CH, Yang j, Cheng Y (2007) Research of an Improved Apriori Algorithm in Data Mining Association Rules, International Conference on Service Systems and Service Management, https://doi.org/10.1109/ICSSSM.2007.4280173.
Ai D, Pan H, Li X. (2018) Association rule mining algorithms on high-dimensional datasets, Artificial Life and Robotics, 23:420–427, https://doi.org/10.1007/s10015-018-0437-y.
Bhandaria A, Guptaa A, Dasa D (2015) Improvised Apriori algorithm using frequent pattern tree for real time applications in data mining, Inter Conf on Infor and Comm Tech, 46: 644-651, https://doi.org/10.1016/j.procs.2015.02.115.
Sun L.n (2020) An improved Apriori algorithm based on support weight matrix for data mining in transaction database, Journal of Ambient Intelligence and Humanized Computing, 11: 495-501, https://doi.org/10.1007/s12652-019-01222-4.
Cheng A, Su S, Xu S, Li Zh (2015) DP-Apriori: A differentially private frequent itemset mining algorithm based on transaction splitting, Computers and Security, 50:74-90, https://doi.org/10.1016/j.cose.2014.12.005.
N.C. Benhamouda, H. Drias, C. Hirèche (2016) Meta-Apriori: A New Algorithm for Frequent Pattern Detection, Intelligent Information and Database Systems. ACIIDS 2016.
Bhalodiya D, Patel K. M, Patel Ch (2013) An Efficient way to Find Frequent Pattern with Dynamic Programming Approach, Nirma University International Conference on Engineering (NUiCONE), https://doi.org/10.1109/NUiCONE.2013.6780102.
Duong H. V, Truong T. C (2015) An efficient method for mining association rules based on minimum single constraint, Vietnam J Comput Sci, 2:67–83, https://doi.org/10.1007/s40595-014-0032-7.
L. Sun (2020) An improved apriori algorithm based on support weight matrix for data mining in transaction database, Journal of Ambient Intelligence and Humanized Computing, 11:495:501. https://doi.org/10.1007/s12652-019-01222-4.
Zh. Jie, W. Gang (2019) Intelligence Data Mining Based on Improved Apriori Algorithm, Journal of Computers, 14:52:62. https://doi.org/10.17706/jcp.14.1.52-62.
J. Dong, M. Han (2020) BitTableFI: An efficient mining frequent itemsets algorithm, Knowledge-Based Systems, 20:329:335. https://doi.org/10.1016/j.knosys.2006.08.005.
Dong J, Han M. (2007) BitTableFI: An efficient mining frequent itemsets algorithm, Knowledge-Based Systems, 20:329–335, https://doi.org/10.1016/j.knosys.2006.08.005.
Liu X, Zhai K, Pedrycz W (2012) An improved association rules mining method, Expert Systems with Applications, 39:1362–1374, https://doi.org/10.1016/j.eswa.2011.08.018.
Liu Y, Li Y, Yang J, Ren Y, Sun G, Li Q (2018) An Improved Apriori Algorithm Based on Matrix and Double Correlation Profit Constraint, Communications in Computer and Information Science, vol 901. Springer, https://doi.org/10.1007/978-981-13-2203-7_27.
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