#4864. Review of association mining methods for the extraction of rules based on the frequency and utility factors

August 2026publication date
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Journal’s subject area:
Management of Technology and Innovation;
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Abstract:
Association rule defines the relationship among the items and discovers the frequent items using a support-confidence framework. This framework establishes user-interested or strong association rules with two thresholds (i.e., minimum support and minimum confidence). Traditional association rule mining methods (i.e., apriori and frequent pattern growth [FP-growth]) are widely used for discovering of frequent itemsets, and limitation of these methods is that they are not considering the key factors of the items such as profit, quantity, or cost of items during the mining process. Such cases, utility-based itemsets mining methods, are playing a vital role in the generation of effective association rules and are also useful in the mining of high utility itemsets. This paper presents the survey on high-utility itemsets mining methods and discusses the observation study of existing methods with their experimental study using benchmarked datasets.
Keywords:
Apriori; Association Rule; FP-Growth; High Utility Itemsets; Support-Confidence Framework

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