文章基本信息

标题：USING K-MEANS ALGORITHM AND FP-GROWTH BASE ON FP-TREE STRUCTURE FOR RECOMMENDATION CUSTOMER SME
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作者：MUHAMMAD ALI SYAKUR ; BAIN KHUSNUL KHOTIMAH ; EKA MALA SARI ROCHMAN 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：4
页码：1102
出版社：Journal of Theoretical and Applied
摘要：The market basket has been finded patterns of purchase customer in SME. Purchase patterns can help to make recommendations and product promotions. This research used K-Means algorithm for sales data clustering and uses FP-Growth Algorithm to know the relation of each cluster. 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. K-Means to do customer segmentation based on background, customer characteristic and level of purchasing power. 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. The research presents a discussion of the comparison of time complexity between FP-Growth algorithms and Apriori Algorithms. This research would been done the development and application of the use of Trees ie FP-Tree (Frequent Pattern Tree). They are an extension of the use of Trees in the data structure. FP-Tree is used in conjunction with the FP-Growth algorithm to determine the frequent itemset of a database, in contrast to the a priori paradigm of scanning the database repeatedly to determine the frequent itemset. In this study, the number of transactions with many items of goods and consumer purchasing power are varied, grouped first by using K-Means algorithm, cluster results formed into several groups including five customer groups based on customer profile. The result of the test is average on minsupp = 60 and minconf = 40, so the average processing time is 957 ms.
关键词：K-Means; Fp-Growth; FP-Tree; SME; Profilling Customer; Pattern