Customer Segmentation is the process of grouping the customers based on their purchase habit. Data mining is useful in finding knowledge from huge amounts of data. The clustering techniques in data mining can be used for the customer segmentation process so that it clusters the customers in such a way that the customers in one group behave similar when compared to the customers in the other group based on their transaction details. The Recency (R), Frequency (F) and Monetary (M) are the important attributes that determine the purchase behavior of the customer. In this, we have provided an improved clustering algorithm for segmenting customers using RFM values and compared the performance against the traditional techniques like K-means, single link and complete link.
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