Random Articles
Reseach Article
A Comprehensive Review of Fuzzy Association Rule Mining Algorithms: Techniques, Applications, and Comparative Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 129 |
| Year of Publication: 2026 |
| Authors: Surati Sandipkumar B., Gangadwala Hardik A. |
10.5120/ijca4418f2ad4f6d
|
Surati Sandipkumar B., Gangadwala Hardik A. . A Comprehensive Review of Fuzzy Association Rule Mining Algorithms: Techniques, Applications, and Comparative Analysis. International Journal of Computer Applications. 187, 129 ( Jul 2026), 24-31. DOI=10.5120/ijca4418f2ad4f6d
Abstract
Association Rule Mining (ARM) is one of the most widely used data mining techniques for discovering hidden relationships and patterns within large datasets. Traditional ARM methods are effective for categorical data but face challenges when handling quantitative and uncertain information. Fuzzy Association Rule Mining (FARM) addresses these limitations by integrating fuzzy logic with association rule mining, enabling the representation of numerical attributes using linguistic terms such as Low, Medium, and High. This paper presents a comprehensive review of major FARM algorithms, including Classical Fuzzy Apriori, Category-List-Linguistic (CLL), Generalized Association Rules (GAR), Fuzzy Generalized Association Rules (FGAR), Fuzzy FP-Growth, NPSFF, FCB, PFCB, SLAVE, Genetic Fuzzy Apriori, MFFI, Load Classifier-Based Algorithms, and Fuzzy Concept-Based Approaches. The study discusses the working principles, advantages, limitations, and application domains of these algorithms. A comparative analysis is also provided based on candidate generation, database scans, scalability, computational efficiency, and suitability for different data types. The review highlights the evolution of FARM techniques from traditional candidate-generation approaches to modern parallel, evolutionary, and concept-based methods designed for big data and streaming environments.
References
- R. Agrawal, T. Imieliński, and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases,” Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207–216, 1993.
- R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), pp. 487–499, 1994.
- X. Fang, 2013. An Improved Apriori Algorithm on the Frequent Itemset, Interneational Conference on Education Technology and Information System.
- K. Niu, H. Jiao, Z. Gao, C. Chen, and H. Zhang, 2017. A Developed Apriori Algorithm Based on Frequent Matrix, ICBCB’2017, January 06-08, 2017.
- J. Han, J. Pei, Y. Yin and R. Mao, 2004. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining Knowledge Discovery, 8: 53-87.
- M.J. Zaki, 2000. Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng., 12: 372-390.
- R. Ishita and A. Rathod, 2016. Eclat with Large Database Parallel Algorithm and Improve its Efficiency, International Journal of Computer Applications, Volume 143-No.13, June 2016.
- Z. Ma, J. Yang, T. Zhang and F. Liu, 2016. An Improved Eclat Algorithm for Mining Association Rules Based on Increased Search Strategy, Internation Journal of Database Theory and Application, Vol. 9, No. 5, 2016.
- P. Shenoy, J. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa and D. Shah, 2000. Turbo-charging vertical mining of large databases. Proceedings of ACM SIGMOD International Conference on Management of Data, MAY 16-18, Dallas, TX., pp: 22-33.
- S. B. Surati and A. A. Desai, Pattern Mining (PM) Algorithm. VNSGU Journal of Science & Technology, Vol. 3, Issue 2, March 2012
- S. B. Surati and A. A. Desai, Pattern Mining using Linked list (PML): mine the frequent patterns from transaction dataset using linked list data structure, 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), July 2017.
- L. A. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
- Y. L. Chen, K. Tang, R. J. Shen, and Y. H. Hu, “Mining fuzzy association rules from questionnaire data,” Knowledge-Based Systems, vol. 22, no. 1, pp. 46–56, 2009.
- C. C. Aggarwal and P. S. Yu, “A New Framework for Itemset Generation,” in Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Madison, WI, USA, 1998, pp. 18–24.
- W. Pedrycz and F. Gomide, Fuzzy Systems Engineering: Toward Human-Centric Computing. Hoboken, NJ, USA: John Wiley & Sons, 2007.
- G. Chen, Q. Wei, and E. E. Kerre, “Fuzzy Association Rules and the Extended Mining Algorithms,” in Intelligent Systems Design and Applications, Berlin, Germany: Springer, pp. 45–62.
- T. P. Hong, C. Y. Lee, and Y. L. Wu, “An Efficient Fuzzy Data Mining Method for Discovering Interesting Association Rules,” Fuzzy Sets and Systems, vol. 138, no. 2, pp. 321–335, 2003.
- J. M. Luna, J. R. Romero, and S. Ventura, “Genetic Algorithms for Fuzzy Association Rule Mining: A Review,” International Journal of Computational Intelligence Systems, vol. 7, no. 1, pp. 9–19, 2014.
- B. Ganter and R. Wille, Formal Concept Analysis: Mathematical Foundations. Berlin, Germany: Springer-Verlag, 1999.
- M. J. Zaki, “Scalable Algorithms for Association Mining,” IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 3, pp. 372–390, 2000.
- J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Waltham, MA, USA: Morgan Kaufmann, 2011.
- A. K. Jain and P. Gupta, “Recent Advances in Fuzzy Data Mining: Techniques, Challenges and Applications,” International Journal of Intelligent Systems and Applications, vol. 15, no. 2, pp. 45–60, 2023.
- S. Sharma and R. Patel, “Big Data Association Rule Mining: A Survey of Scalable Algorithms and Frameworks,” Journal of Big Data Analytics, vol. 8, no. 1, pp. 112–130, 2024.
- L. Rutkowski and M. Korytkowski, “Explainable Artificial Intelligence and Fuzzy Systems: Opportunities and Challenges,” IEEE Access, vol. 12, pp. 34567–34589, 2024.
Index Terms
Keywords