Application of Fuzzy C-Means Algorithm for Clustering Customers

Oktaviana Nirmala Purba, Dian Novianti Sitompul, Tua Holomoan Harahap, Sri Rahmah Dewi Saragih, Rizka Fahruza Siregar
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Abstract

Today's fierce business competition requires companies to focus on the needs desired by consumers.This makes companies have to think about how to manage customer data so that it can be utilized properly for the development of marketing strategies.And Grouping (cluster) customers based on their respective characteristics can be an alternative in solving these problems.In clustering (cluster) customers there are several data mining clustering methods that can be used, one of which is the Fuzzy C-Means (FCM) method. FCM is a clustering algorithm where one object can be a member of several clusters and FCM cluster boundaries are vague. The output of FCM is a row of cluster centers and several degrees of membership for each data point. In this clustering, customers will be divided into 4 customer clusters namely Golden, Silver, Bronze, and Iron with the variables used as a reference are the final purchase date, purchase frequency and total purchase. The data used is customer transaction data for the period September - December 2015. The total data is 709 transactions from 75 customers. After the data is processed with the Fuzzy C-Means method, the final results show that the iteration ends at the 30th iteration with a change in the objective function of 9.8. The resulting customer clusters are Golden: 27, Silver: 15, and Bronze: 33 with a cluster validity of 0.596277.
模糊c均值算法在客户聚类中的应用
当今激烈的商业竞争要求公司关注消费者的需求。这使得企业不得不考虑如何管理客户数据,以便能够适当地利用它来制定营销策略。根据客户各自的特征对客户进行分组(集群)是解决这些问题的另一种选择。在客户聚类中,有几种数据挖掘聚类方法可以使用,其中一种是模糊c均值(FCM)方法。FCM是一种聚类算法,其中一个对象可以是多个聚类的成员,FCM聚类边界是模糊的。FCM的输出是一排聚类中心和每个数据点的几个隶属度。在这个聚类中,客户将被分为4个客户集群,分别是Golden、Silver、Bronze和Iron,作为参考的变量是最终购买日期、购买频率和总购买次数。使用的数据是2015年9月至12月期间的客户交易数据。总的数据是来自75个客户的709笔交易。用模糊c均值法对数据进行处理后,最终结果表明,迭代结束于第30次迭代,目标函数的变化量为9.8。得到的客户集群是金色:27,银色:15和青铜:33,集群有效性为0.596277。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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