An Algorithm for Fuzzy Association Rules Extraction Based on Prime Number Coding
Imen Mguiris, Hamida Amdouni, Mohamed Mohsen Gammoudi · 2017
Fuzzy association rules are one of the most important data mining techniques. They allow to discover useful and meaningful information that help in decision-making. Many algorithms have been proposed to extract fuzzy association rules. A major drawback of these proposed algorithms is their high run-time for extracting fuzzy association rules. To overcome this problem, we introduce in this paper a novel algorithm based on Fuzzy Formal Concept Analysis and Prime Number Coding. The principle of our algorithm is based on three steps: (i) Frequent fuzzy minimal generators extraction. (ii) Construction of the frequent fuzzy minimal generators lattice. (iii) Generation of exact and transitive fuzzy association rules.