Treffer: An empirical analysis toward mining association rules for market basket analysis through statistical measures.

AbstractThe value of data resources is enormous within the framework of the digital age, and the utilization of these resources has given rise to Data Mining. It is the practice of extracting helpful paradigms and insightful information from massive datasets to inform recommendations. Association rule mining is a recipe to pinpoint links between patterns. The method for mining data called market basket analysis (MBA) is commonly employed in the retailing industry to understand the purchasing habits of consumers. This paper presents an overview of three popular market basket analysis algorithms: Apriori, FP-Growth, and Eclat. Existing research shows that Apriori takes more run time and fewer frequent itemsets. We used three datasets with different transaction sizes and the number of unique items at different thresholds to find the frequent and interesting item sets. The datasets and results are tested on statistical tools like confidence intervals, variance. The result shows that Apriori is more efficient compared to Frequent Pattern Growth and Eclat algorithms in the context of run time, frequent itemset finding, and memory consumption. Eclat is the highest time-taking algorithm. Apriori is a good algorithm for large, sparse datasets. [ABSTRACT FROM AUTHOR]

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