Association Rule Mining Using Retail Market Basket Dataset by Apriori and FP Growth Algorithms

Abstract

Data mining is a method for dealing with large amounts of data. It takes data from a large dataset and extracts meaningful information. A suite of algorithms has been developed to extract meaningful information from big datasets. Apriori, ECLAT, FPGrowth, and others are examples of such algorithms. These algorithms are mostly used to identify the frequent itemsets. There are two models and eight functions in data mining, and each model has four different functions. In this study, we will employ one Apriori technique and the other, the FPGrowth algorithm, to find frequent itemsets. These methods operate on the same dataset in different ways, extracting the same frequent itemsets but with varied execution times. The remainder of this work is organized logically. The rest of the work is arranged in the following manner: The first segment begins with an introduction. The second segment is a review of related literature. The third segment looks into the foundational ideas. The outcome and analysis are depicted in Section 4. Finally, Section 5 brings this paper to a close by discussing the implications of our work for future research endeavors.