Copyright ©2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. DOI: 10.4018/978-1-4666-9888-8.ch013 Chapter 13 256 Customer Behavior Prediction using K-Means Clustering Algorithm ABSTRACT Due to the increased availability of individual customer data, it is possible to predict customer buying pattern. Customers can be segmented using clustering algorithms based on various parameters such as Frequency, Recency and Monetary values (RFM). The data can further be analyzed to infer rules among two or more purchases of the customer. In this chapter we will present a clustering algorithm, enhanced k- means algorithm, which is based on k- means algorithm to divide cus- tomers into various segments. After segmentation, each segment is mined with the help of a priori algorithm to infer rules so that the customer’s purchase behavior can be predicted. From large number of association rules with sufcient coverage, the customer’s purchasing pattern can be predicted. Experiment on real database is implemented to evaluate the performance on efectiveness and utility of the ap- proach. The results show that the proposed approach can gain a well insight into customers’ segmentation and thus their behavior can be predicted. Juhi Singh Banasthali Vidyapith, India Mandeep Mittal Amity School of Engineering and Technology, India Sarla Pareek Banasthali Vidyapith, India