Comparison of Apriori Algorithm and FP-Growth in Managing Store Transaction Data

Syukron Anas, Nelson Rumui, Andi Roy, Pujo Hari Saputro

Abstract

The role and position of data in today's digital era are very important, data can be likened to a resource that can be explored further to produce new information or knowledge. Seeing the importance of data position, several solutions can be offered in getting more value from data, one of which is the use of Data Mining techniques with association techniques, several types of association techniques are a priori algorithms and FP-Growth algorithms. Based on the research results, the a priori algorithm produces a combination of goods with a confidence value of 98.4 and a support value of 98.4, and the algorithm produces a combination of goods with a support value of 95.2 and a confidence value of 95.2. The comparison of these two algorithms in making associations results in a faster execution time of the FP-Growth algorithm than Apriori, and the Apriori algorithm produces more varied itemset combinations.

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