文章基本信息

标题：E-FWARM: ENHANCED FUZZY-BASED WEIGHTED ASSOCIATION RULE MINING ALGORITHM
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作者：K. MANGAYARKKARASI ; M. CHIDAMBARAM
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：2018
卷号：96
期号：2
页码：322
出版社：Journal of Theoretical and Applied
摘要：In the Association Rule Mining (ARM) approach, equal weight is assigned to all itemsets in the dataset. Hence, it is not appropriate for all datasets. The weight should be assigned based on the significance of each itemset. The WARM reduces extra steps during the generation of rules. As, the Weighted ARM (WARM) uses the significance of each itemset, it is applied in the data mining. The Fuzzy-based WARM satisfies the downward closure property and prunes the insignificant rules by assigning the weight to the itemset. This reduces the computation time and execution time. This paper presents an Enhanced Fuzzy-based Weighted Association Rule Mining (E-FWARM) algorithm for efficient mining of the frequent itemsets. The pre-filtering method is applied to the input dataset to remove the item having low variance. Data discretization is performed and E-FWARM is applied for mining the frequent itemsets. The experimental results show that the proposed E-FWARM algorithm yields maximum frequent items, association rules, accuracy and minimum execution time than the existing algorithms.
关键词：Association Rule Mining (ARM); Data Mining; Frequent Itemset Mining; Fuzzy-based Weighted Association Rule Mining (FWARM)