Performance comparison of association rule mining algorithms among Apriori, FP-Growth, FP-Max, and H-Mine for market basket analysis
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Abstract
Association rule mining is a crucial technique for market basket analysis in retail businesses, but it often faces challenges in processing speed and memory usage, particularly with large-scale datasets. This research presents a performance comparison of four algorithms: Apriori, FP-Growth, FP-Max, and H-Mine, using a grocery store dataset for market basket analysis under varying support thresholds. The results showed that the H-Mine algorithm demonstrated superior performance in both execution time and memory usage, attributed to its efficient Hyperlink data structure, followed by FP-Growth and FP-Max algorithms, which employ FP-Tree structure to minimize database scanning. Meanwhile, the Apriori algorithm exhibited the lowest performance.
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References
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