Author:
Bouaita Bilal,Beghriche Abdesselem,Kout Akram,Moussaoui Abdelouahab
Abstract
Association rule methods are among the most used approaches for Knowledge Discovery in Databases (KDD), as they allow discovering and extracting hidden meaningful relationships between attributes or items in large datasets in the form of rules. Algorithms to extract these rules require considerable time and large memory spaces. This paper presents an algorithm that decomposes this complex problem into subproblems and processes items by category according to their support. Very frequent items and fairly frequent items are studied together. To evaluate the performance of the proposed algorithm, it was compared with Eclat and LCMFreq on two actual transactional databases. The experimental results showed that the proposed algorithm was faster in execution time and demonstrated its efficiency in memory consumption.
Publisher
Engineering, Technology & Applied Science Research
Reference27 articles.
1. A. Alqahtani, H. Alhakami, T. Alsubait, and A. Baz, "A Survey of Text Matching Techniques," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6656–6661, Feb. 2021.
2. R. Agrawal, T. Imieliński, and A. Swami, "Mining association rules between sets of items in large databases," in Proceedings of the 1993 ACM SIGMOD international conference on Management of data, New York, NY, USA, Mar. 1993, pp. 207–216.
3. S. Chakraborty, S. H. Islam, and D. Samanta, "Introduction to Data Mining and Knowledge Discovery," in Data Classification and Incremental Clustering in Data Mining and Machine Learning, S. Chakraborty, S. H. Islam, and D. Samanta, Eds. Cham, Switzerland: Springer International Publishing, 2022, pp. 1–22.
4. H. Alizadeh and B. M. Bidgoli, "Introducing A Hybrid Data Mining Model to Evaluate Customer Loyalty," Engineering, Technology & Applied Science Research, vol. 6, no. 6, pp. 1235–1240, Dec. 2016.
5. C. Kenneth and O. Chinecherem, "Knowledge Discovery in Databases (KDD): An Overview," International Journal of Computer Science and Information Security, vol. 15, no. 12, pp. 13–16, Dec. 2017.