A Comparative Survey on K-Means and Hierarchical Clustering in E-Commerce Systems

E-commerce systems have grown in popularity and are now used in almost every business. A platform for online product marketing and customer promotion is an e-commerce system. Customer clustering is defined as the process of categorizing consumers into sections that share resembling characteristics. To maximize each customer's profit to the business, customer clustering’s goal is to help decide how to engage clients in each category. To facilitate customer needs by improvising products and optimizing services, businesses can identify their most profitable customers by segmenting their customer base. As a result, customer clustering assists E-commerce systems in promoting the appropriate product to the appropriate customer to increase profits. Customer clustering factors include geographic, psychological, behavioral, and demographic factors. The consumer’s behavioral factor has been highlighted in this research. As a result, to discover the consumption behavior of the E-shopping system, customers will be analyzed using several clustering algorithms. Clustering seeks to maximize experimental similarity within a cluster while minimizing dissimilarity between clusters. Customers’ age, gender, income, expenditure rate, etc. are correlated in this study. To assist vendors in identifying and concentrating on the most profitable segments of the market as opposed to the least profitable segments, this study compared several clustering techniques to find which technique is more accurate to cluster customer behavior. A significant role for this kind of analysis in business improvement to keep customers for a long time and boost business profits, businesses group their customers based on similar behavioral traits. It also enables the maximum disclosure of online offers to attract the attention of potential customers. A learning algorithm called K-Means and an unsupervised algorithm hierarchical clustering is applied to a customer dataset to compare which strategy gives most accurate clustering.