Customer Clustering Using a Combination of Fuzzy C-Means and Genetic Algorithms

Abstract

<p>This study intends to combine the fuzzy c-means clustering and genetic algorithms to cluster the customers of steel industry. The customers were divided into two clusters by using the variables of the LRFM (length, recency, frequency, monetary value) model. Results indicated that customers belonging to the first cluster had a higher length of the relationship, recency of trade, and frequency of trade but lower monetary value compared to the average values of these criteria for all customers. The results also showed that customers belonging to the second<br />cluster had a higher recency of trade and monetary value but lower length of the relationship and frequency of trade compared to the average values of these criteria for all customers. It was also found that the combined algorithm (i.e., fuzzy c-means clustering and genetic algorithm) used in this study had a lower mean squared error (MSE) compared to fuzzy c-means clustering.</p>