2021
DOI: 10.1080/24751839.2021.1937465
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A General Method for mining high-Utility itemsets with correlated measures

Abstract: Discovering high-utility itemsets from a transaction database is one of the important tasks in High-Utility Itemset Mining (HUIM). The discovered high-utility itemsets (HUIs) must meet a user-defined given minimum utility threshold. Several methods have been proposed to solve the problem efficiently. However, they focused on exploring and discovering the set of HUIs. This research proposes a more generalized approach to mine HUIs using any user-specified correlated measure, named the General Method for Correla… Show more

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Cited by 4 publications
(1 citation statement)
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“…Since the first FIM algorithm, Apriori [2], many such algorithms have been presented in the last two decades. These algorithms are classified into different types, such as sequential patterns mining algorithms [24,25], data stream mining algorithms [26,27], graph mining algorithms [28,29], approximate frequent itemset mining in uncertain data [30,31], and high utility frequent itemset mining algorithms [32,33]. In this section, we present the FIM algorithms relevant to the research presented in this paper.…”
Section: Related Workmentioning
confidence: 99%
“…Since the first FIM algorithm, Apriori [2], many such algorithms have been presented in the last two decades. These algorithms are classified into different types, such as sequential patterns mining algorithms [24,25], data stream mining algorithms [26,27], graph mining algorithms [28,29], approximate frequent itemset mining in uncertain data [30,31], and high utility frequent itemset mining algorithms [32,33]. In this section, we present the FIM algorithms relevant to the research presented in this paper.…”
Section: Related Workmentioning
confidence: 99%