Utilizing FP-Tree and FP-Growth Algorithms for Data Mining on Medicine Sales Transactions at Khanina’s

Abstract

Although Khanina Pharmacy is a growing pharmacy with a lot of processes, the data processing is still done by hand. This study examines the use of the FP-Tree and FP-Growth algorithms to the medication sales transaction system. The FP-Tree and FP-Growth algorithm methods use methods or strategies to choose data in order to identify trends or intriguing details. The FP-Tree and FP-Growth algorithm approaches are two frequently used techniques in data mining. The purpose of this medicine sales transaction data is to identify concurrently purchased products. The FP-Growth Algorithm is used to find item pattern combinations. Use of FP-Tree to identify frequently occurring itemsets from a database in combination with the FP-Growth algorithm. When searching for product attachment patterns for sales tactics in decision-making rules, the Association Rule method is employed. In order to determine which medications are frequently bought by customers, we can create rules using the data in the database. The Rapidminer 5 program was used to conduct the test. This test yielded the following results: the number of itemsets created and rules constructed increased with decreasing support values.