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WIREs Data Mining Knowl Discov
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A survey on association rules mining using heuristics

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Association rule mining (ARM) is a commonly encountred data mining method. There are many approaches to mining frequent rules and patterns from a database and one among them is heuristics. Many heuristic approaches have been proposed but, to the best of our knowledge, there is no comprehensive literature review on such approaches, yet with only a limited attempt. This gap needs to be filled. This paper reviews heuristic approaches to ARM and points out their most significant strengths and weaknesses. We propose eight performance metrics, such as execution time, memory consumption, completeness, and interestingness, we compare approaches against these performance metrics and discuss our findings. For instance, comparison results indicate that SRmining, PMES, Ant‐ARM, and MDS‐H are the fastest heuristic ARM algorithms. HSBO‐TS is the most complete one, while SRmining and ACS require only one database scan. In addition, we propose a parameter, named GT‐Rank for ranking heuristic ARM approaches, and based on that, ARMGA, ASC, and Kua emerge as the best approaches. We also consider ARM algorithms and their characteristics as transactions and items in a transactional database, respectively, and generate association rules that indicate research trends in this area. This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Comparison of execution time with apriori and FP‐growth
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Average memory consumption
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Comparison from the point of view of number of rules/itemsets
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Number of database scans
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Comparison based on the fitness values
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Comparison of GA‐based and PSO‐based ARM algorithms
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First execution time comparison
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Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Technologies > Association Rules
Algorithmic Development > Association Rules

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