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In this modern era, everything and everyone is innovative, where everyone competes
with being better than others. The emergence of many entrepreneurs, competitors,
and business interested people has created a lot of insecurities and tension among
competing businesses to find new customers and hold the old customers. Because of
this one should need and maintain exceptional customer service and it becomes very
appropriate irrespective of the business scale. And also, it is equally important to
understand the needs of customers specifically to provide greater customer support
and to advertise them with the most appropriate products. In the pool of these online
products customers are confused about what to buy and what not to and also the
company or the business people are confused about which section of customers to
be targeted for selling their particular type of products. This confusion will probably be
possible by the process called CUSTOMER SEGMENTATION. The process of
segmenting the customers with similar interests and similar shopping behavior into
the same segment and with different interests and different shopping patterns into
different segments is called customer segmentation. Customer segmentation and
pattern extraction are the major aspects of a business decision support system. Each
segment has the same set of customers who most probably has the same kind of
interests and shopping patterns. In this paper, we planned to do this customer
segmentation using three different clustering algorithms namely K means clustering
algorithm, Mini batch means, and hierarchical clustering algorithms and also going to
compare all these clustering algorithms based on their efficiency and root mean
squared errors.

Keywords: Customer segmentation, Clustering, K-means clustering, Mini Batch
Kmeans clustering, hierarchical clustering

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