References
- Man, M., Jalil, M.A. and Bakar, W.A. (2023) “Fi-Eclat: An enhancement of Incremental Eclat algorithm,” 1ST INTERNATIONAL POSTGRADUATE CONFERENCE ON OCEAN ENGINEERING TECHNOLOGY AND INFORMATICS 2021 (IPCOETI 2021) [Preprint]. Available at: https://doi.org/10.1063/5.0110230.
- Jain, P., Gyanchandani, M. and Khare, N. (2016) “Big Data Privacy: A Technological Perspective and Review,” Journal of Big Data, 3(1). Available at: https://doi.org/10.1186/s40537-016-0059-y.
- Yusof, M.K. (2017) “Efficiency of JSON for data retrieval in Big Data,” Indonesian Journal of Electrical Engineering and Computer Science, 7(1), p. 250. Available at: https://doi.org/10.11591/ijeecs.v7.i1.pp250-262.
- Srinadh, V. (2022) “Evaluation of Apriori, FP growth and Eclat Association rule mining algorithms,” International journal of health sciences, pp. 7475–7485. Available at: https://doi.org/10.53730/ijhs.v6ns2.6729.
- Borgelt, C. (2003). Efficient Implementations of Apriori and Eclat. Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations; 19 November 2003.
- Kumbhare, T.A. and Chobe, S.V., (2014) An overview of association rule mining algorithms. International Journal of Computer Science and Information Technologies, 5(1), pp.927-930.x
- Chun-Sheng, Z. and Yan, L. (2014) “Extension of local association rules mining algorithm based on Apriori algorithm,” 2014 IEEE 5th International Conference on Software Engineering and Service Science [Preprint]. Available at: https://doi.org/10.1109/icsess.2014.6933577.
- R. Ishita, and A. Rathod, International Journal of Computer Applications 143, 33-37 (2016).
- M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, Data Mining and Knowledge Discovery 1, 343-373 (1997).
- K. Maniktala, J. Singh, and R. K. Gurm, International Journal of Technology and Computing 2, 547-548 (2016).
- W. A. W. A. Bakar, M. Man, and Z. Abdullah, Telkomnika 18, 562-570 (2020).
- M. Benjamin, High-speed inserts with MySQL, Available: https://medium.com/@benmorel/high-speed-insertswith-mysql-9d3dcd76f723 (Accessed 17 Jan 2023).
- W. A. B. W. A. Bakar, Z. Abdullah, M. Y. B. M. Saman, M. Man, T. Herawan, and A. R. Hamdan, “Incremental-eclat model: an implementation via benchmark case study,” in Advances in Machine Learning and Signal Processing, edited by J. S. Ping, L. W. Wai, H. A. Sulaiman, M. A. Othman, and M. S. Saat (Springer International Publishing, Switzerland, 2016), pp. 35-46.
- Panjaitan, S., Sulindawaty, Amin, M., Lindawati, S., Watrianthos, R., Sihotang, H. T., & Sinaga, B. (2019). Implementation of apriori algorithm for analysis of Consumer Purchase Patterns. Journal of Physics: Conference Series, 1255(1), 012057. https://doi.org/10.1088/1742-6596/1255/1/012057
- Wang, H.-B., & Gao, Y.-J. (2021). Research on parallelization of Apriori algorithm in Association Rule Mining. Procedia Computer Science, 183, 641–647. https://doi.org/10.1016/j.procs.2021.02.109
- Haykin, S. Neural Networks: A Comprehensive Foundation, 2nd ed.; Prentice Hall PTR: Upper Saddle River, NJ, USA, 1999.
- Beyond the Maximum Storage Capacity Limit in Hopfield Recurrent Neural Networks
- Hu X, Feng G, Li H, Chen Y, Duan S (2014) An adjustable memristor model and its application in small-world neural networks. In: 2014 international joint conference on neural networks (IJCNN). Beijing, China
- Fekete T, Beacher FDCC, Cha J, Rubin D, Mujica-Parodi LR (2014) Small-world network properties inprefrontal cortex correlate with predictors of psychopathology risk in young children: a NIRS study. Neuroimage 85:345–353
- Taylor NR (2013) Small world network strategies for studying protein structures and binding. Comput Struct Biotechnol J5(6):1–7
- Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390. doi:10.1109/69.846291.
- Zhang, X., Tang, Y., Liu, Q., Liu, G., Ning, X., & Chen, J. (2021). A fault analysis method based on association rule mining for distribution terminal unit. Applied Sciences (Switzerland), 11(11), 5221. doi:10.3390/app11115221.
- Li, Z. F., Liu, X. F., & Cao, X. (2011). A study on improved Eclat data mining algorithm. Advanced Materials Research, 328-330, 1896–1899. https://doi.org/10.4028/www.scientific.net/amr.328-330.1896
- Gayathri, G. (2017). Performance comparison of Apriori, Eclat and FPGrowth algorithm for association rules learning. International Journal of Computer Science and Mobile Computing, 81-89.
- Robu, V., dos Santos, V. D. (2019). Mining frequent patterns in data using apriori and Eclat: A comparison of the algorithm performance and Association Rule Generation. 2019 6th International Conference on Systems and Informatics (ICSAI). https://doi.org/10.1109/icsai48974.2019.9010367
- Sinthuja, M., Puviarasan, N., Aruna P. (2017). Evaluating the Performance of Association Rule Mining Algorithms. World Applied Sciences Journal 35 (1): 43-53. Doi: 10.5829/idosi.wasj.2017.43.53