FREQUENT PATTERN ANALYSIS ON SALES DATA USING ECLAT ALGORITHMS TO DETERMINE SALES STRATEGIES - Imam Fahrur Rozi, Yan Watequlis Syaifudin, Nursita Al Mufidah (International Conference on Industrial Revolution for Polytechnic Education, 2019)
Sales transaction data is one of the data that can be processed and analyzed as the object of a study. ECLAT algorithm is a development algorithm from the Apriori Algorithm which is often used to analyze sales transaction data. The ECLAT algorithm is an algorithm that uses a vertical data format to represent the data. The advantages of the ECLAT Algorithm are the process and performance of calculating support for all itemsets carried out in a more efficient way compared to the Apriori HUI-Miner Algorithm. ECLAT algorithm is used to help find frequent patterns on sales data, the results of which are product purchase rules that are often purchased simultaneously by consumers in one transaction. By using association rule mining results can be determined from the value of support and confidence of a product rule obtained. Based on the results of data analysis, the minimum effect of support for confidence and lift ratio is obtained if the higher the minimum support value is used, the more likely the resulting rule is. However, the percentage of rule results that get the test value of lift ratio above 1.00 is only a little. Meanwhile, if the minimum support value is used, then the possibility of the percentage rule that has a lift ratio above 1.00 more and more can be said to be a valid rule to be used as a suggestion for the rule of goods to be discounted.