Implementation Method and Comparative Experiment Based on Association Rule Mining Algorithm

DOI: https://doi.org/10.62517/jbdc.202601131

Author(s)

Yongfu Liu, Jian Jiao

Affiliation(s)

Gansu Open University, Lanzhou, Gansu, China

Abstract

With the rapid development of the information age and the continuous improvement of data storage technology, association rule mining algorithms, as an important data mining technique, play a key role in discovering the relationships between items in a dataset. This article aims to explore the implementation methods of association rule mining algorithms and analyze the advantages, disadvantages, and applicability of different algorithms through comparative experiments. In order to compare and analyze the performance of these two algorithms, this paper designed a set of comparative experiments. The experimental dataset contains multiple transactions, each consisting of a set of items. The experiment evaluates the performance of different algorithms by calculating their efficiency, accuracy, and memory usage in discovering frequent itemsets and association rules. The experimental results show that the Apriori algorithm is relatively simple to implement, easy to understand and implement, and therefore still has certain application value when dealing with small-scale datasets.

Keywords

Association Rule Mining; Apriori Algorithm; Implementation Method; Comparative Experiment

References

[1] Wang, J, & Wang, Y. (2020). An Improved Apriori Algorithm for Mining Association Rules in Educational Data. Journal of Physics: Conference Series, 1646(1), 012016. [2] Zhang, L, & Liu, Y. (2021). An Efficient Algorithm for Mining Association Rules with Constraints in Large Databases. International Journal of Database Theory and Application, 14(1), 1-12. [3] Li, M, & Chen, L. (2022). A Novel Approach for Mining Frequent Itemsets with FP-Growth Algorithm in Healthcare Data. Journal of Healthcare Informatics Research, 6(1), 1-13. [4] Wu, H, & Zhang, W. (2020). Data Association Rules Mining Method Based on Improved Apriori Algorithm. In Proceedings of the 4th International Conference on Big Data Research (pp. 1-6). DOI: 10.1145/3445945.3445948. [5] Chen, X, & Liu, B. (2021). An Efficient Parallel Algorithm for Mining Association Rules in Big Data Environments. IEEE Transactions on Knowledge and Data Engineering, 33(1), 136-149. [6] Liu, Y, & Wang, J. (2022). A Novel Hybrid Algorithm for Mining Association Rules in E-commerce Data. Journal of Electronic Commerce Research, 19(1), 56-70. [7] Zhang, Q, & Li, H. (2020). An Improved Apriori Algorithm Based on Matrix Transposition for Mining Association Rules. Journal of Computer Science and Technology, 35(6), 1291-1302. [8] Wang, L, & Sun, J. (2021). An Efficient Algorithm for Mining Association Rules in High-Dimensional Data. Information Sciences, 560, 19-33. [9] Yang, M, & Zhang, Y. (2022). A Parallel FP-Growth Algorithm for Mining Association Rules in Large Databases. Journal of Parallel and Distributed Computing, 156, 1-12. [10] Li, X, & Wang, Y. (2023). An Enhanced Apriori Algorithm for Mining Association Rules in Sparse Datasets. Knowledge-Based Systems, 253, 109406. [11] Agrawal, R, Imieliński, T, & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207-216. [12] Han, J, Pei, J, & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1-12. [13] Zaki, M. J. (1999). SPADE: An efficient algorithm for mining frequent sequences. Machine Learning, 35(1-2), 121-145. [14] Srikant, R, & Agrawal, R. (1996). Mining generalized association rules. Proceedings of the 22nd International Conference on Very Large Data Bases (VLDB'96), 407-419. [15] Fournier-Viger, P, Wu, C.-W, Zida, S., & Nkambou, R. (2014). A survey of itemset mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(5), 351-377