期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2021
卷号：99
期号：2
页码：316
出版社：Journal of Theoretical and Applied
摘要：Data mining (DM) is an analysis extensive data in order to gain the novel and hidden information. DM becomes vital to a lot of research domain like soft computing, artificial intelligence, statistics and machine learning. One of the important topics of DM is Association Rule Mining (ARM) in mega databases where it is used to discover frequent itemsets using statistical metrics such as support (Sup) and confidence (Conf) which is an essential process in ARM. Also ARM is practiced to produce association rules (ARs) from frequent itemsets. Such ARs reveal a link between items in the real world. Several algorithms have been submitted to achieve these ARs. However, these algorithms suffer from redundancy problems and a large number of derived ARs, which makes the algorithms ineffective and complicated them for the end users to understand the rules that were created. Because of these motives, this paper uses the type-2 fuzzy association rules mining technique (T2FARM) to achieve frequent itemsets and identify all relationships between items and ARs that achieve minimum support (min sup) and minimum confidence (min conf) in addition to pruning redundant rules. And also adapts genetic algorithm (GA) to improve non-redundant rules derived. Empirical evaluations display that the proposed technique improves redundant rules pruning of DM compared to traditional fuzzy association rules (FARs) and able to improve non-redundant rules by GA.
关键词：Association Rule; Apriori Algorithm; Type-2 Fuzzy linguistic; Redundancy of Fuzzy Association Rules; Genetic Algorithm.
