Application of Fuzzy C-Means Algorithm for Clustering Customers
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DOI:
https://doi.org/10.56211/hanif.v1i1.8Keywords:
Data Mining; Fuzzy C-Means (FCM); Customer Cluster; Cluster Validity; Transaction; RFM
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.
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