Application of Association Rule Method Using the ECLAT Algorithm on Over-the-Counter Drug Transaction Data at Pharmacy “X”

Health is a fundamental need for every human being that plays an important role in improving the quality of life and productivity of society. As one of the means of pharmaceutical services, pharmacies not only serve as places for drug distribution but also as abundant sources of information regarding community purchasing patterns for health products. In the current digital era, every transaction that occurs at a pharmacy generates high-value data that can be utilised for data-driven decision making. One of the relevant analytical approaches in this context is Market Basket Analysis (MBA). Association rule is a commonly used method in MBA. This method generates rules in the form of implications "if X, then Y" based on the frequency of item occurrences in the data. The algorithm that can be used to perform association rule mining is ECLAT (Equivalence Class Clustering and bottom-up Lattice Traversal). Based on the results of the descriptive analysis of non-prescription drug transaction data from Pharmacy "X," it is known that the drugs frequently purchased by consumers are those containing paracetamol. Next, the association rule with the ECLAT algorithm with a minimum support of 0,0004 and a minimum confidence of 0,5 produces three rules that reflect that these drugs are often purchased together by consumers of Pharmacy "X".