They are an extension of the use of Trees in the data structure. This research would been done the development and application of the use of Trees ie FP-Tree (Frequent Pattern Tree). The research presents a discussion of the comparison of time complexity between FP-Growth algorithms and Apriori Algorithms. To facilitate the analysis of customer relationships with products purchased, then each cluster profilling customer will be processed data record by FP Growth to know the relevance of goods purchased. K-Means to do customer segmentation based on background, customer characteristic and level of purchasing power. K-Means clustering to classify customer data based on the same attribute, then determined the relationship between patterns in each group with FP-Growth Algorithm. This research used K-Means algorithm for sales data clustering and uses FP-Growth Algorithm to know the relation of each cluster. Purchase patterns can help to make recommendations and product promotions. The market basket has been finded patterns of purchase customer in SME.
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