Empirical Review of Trends in the Implementation of Frequent Pattern Mining for E-Commerce
Aftab Ahmed N.A.
Ahmed
Dr. Syed Umar
Himalayan University, Itanagar, Arunachal Pradesh
41-45
Vol: 11, Issue: 1, 2021
Receiving Date:
2021-02-08
Acceptance Date:
2021-03-24
Publication Date:
2021-03-29
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http://doi.org/10.37648/ijrst.v11i01.005
Abstract
Customers commonly need to gather even more details about an item prior to purchasing. They usually reflect on the view of additional customers to help to make a decision on their buy. Today, various websites possess been lately created that emphasis the involvement of end users. Several of the websites many of these as Amazon.com prospects persons to create their judgment about the items and talk about regarding the features of that merchandise. It gives a grasp info sources on the internet. Acquiring all such reviews assists manufacturers to be conscious of the weakness and strengths of their solution to increase it.
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Keywords:
Data mining; frequent pattern mining; sampling; association rule mining
References
- Changhai, Huang, and Hu Shenping. 'Factors correlation mining on maritime accidents database using association rule learning algorithm.' Cluster Computing 22.2 (2019): 4551-4559.
- D’Angelo, Gianni, et al. 'A data-driven approximate dynamic programming approach based on association rule learning: Spacecraft autonomy as a case study.' Information Sciences 504 (2019): 501-519.
- Yu, Ziwen, et al. 'Automated detection of unusual soil moisture probe response patterns with association rule learning.' Environmental Modelling & Software 105 (2018): 257-269.
- Jeon, Minsoo. 'Analysis of point deduction patterns in Taekwondo using association rule learning.' International Journal of Performance Analysis in Sport 19.3 (2019): 323-330.
- Alkhamees, Nora, and Maria Fasli. 'The Dynamic-FPM: An Approach for Identifying Events from Social Networks Using Frequent Pattern Mining and Dynamic Support Values.' 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019.
- Ovi, Jesan Ahammed, et al. 'Mining weighted frequent patterns from uncertain data streams.' International Conference on Ubiquitous Information Management and Communication. Springer, Cham, 2019.
- Leung, Carson Kai-Sang, Richard Kyle MacKinnon, and Syed K. Tanbeer. 'Fast algorithms for frequent itemset mining from uncertain data.' 2014 IEEE International Conference on Data Mining. IEEE, 2014.
- Lee, Gangin, Unil Yun, and Heungmo Ryang. 'An uncertainty-based approach: frequent itemset mining from uncertain data with different item importance.' Knowledge-Based Systems 90 (2015): 239-256.
- Li, Haifeng, et al. 'Probabilistic frequent itemset mining over uncertain data streams.' Expert Systems with Applications 112 (2018): 274-287.
- Leung, Carson Kai-Sang, and Dale A. Brajczuk. 'Mining uncertain data for constrained frequent sets.' Proceedings of the 2009 International Database Engineering & Applications Symposium. 2009.
- Luna, José María, Philippe Fournier‐Viger, and Sebastián Ventura. 'Frequent itemset mining: A 25 years review.' Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9.6 (2019): e1329.
- Gan, Wensheng, et al. 'A survey of parallel sequential pattern mining.' ACM Transactions on Knowledge Discovery from Data (TKDD) 13.3 (2019): 1-34.
- Choi, Hyeok-Jun, and Cheong Hee Park. 'Emerging topic detection in twitter stream based on high utility pattern mining.' Expert systems with applications 115 (2019): 27-36.
- Xiao, Zhe, et al. 'Traffic pattern mining and forecasting technologies in maritime traffic service networks: A comprehensive survey.' IEEE Transactions on Intelligent Transportation Systems 21.5 (2019): 1796-1825.
- Zadeh, Amir Hassan, et al. 'Characterizing basal-like triple negative breast cancer using gene expression analysis: A data mining approach.' Expert Systems with Applications 148 (2020): 113253.
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