Customer segmentation using centroid based and density based clustering algorithms

Abstract

In recent years, customer segmentation has become one of the most significant and useful tools for e-commerce. It plays a vital role in online product recommendation system and also helps to understand local and global wholesale or retail market. Customer segmentation refers to grouping customers into different categories based on shared characteristics such as age, location, spending habit and so on. Similarly, clustering means putting things together in such a way that similar type of things remain in the same group. Due to having similarities between these two terms, it is possible to apply clustering algorithms for ensuring satisfactory and automatic customer segmentation. Among different types of clustering algorithms, centroid based and density based are the most popular. This paper illustrates the idea of applying density based algorithms for customer segmentation beside using centroid based algorithms like k-means. Applying DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm as one of the density based algorithms results in a meaningful customer segmentation.