Customer Data Clustering using Density based algorithm

Abstract

This paper is about Clustering different segments of customers and their patterns of behaviour over different time intervals which are a very important application for business to maintain Business to Customer (B2C) Relationship. For clustering different segments of customers the input data will be taken from various business organizations like smart retail stores, and other stores. We take the input data from a particular amount of time like a year's data. All this data will be taken from the organization's databases. It has been observed that maintaining the old customers generate more profit when compared to attracting new ones. So, Customer retention is the important factor in our project. The main objective is to identify the elements with high profit and value to group them into different clusters. This will help to identify the high-value low-risk customers. From the results obtained, we can be able to propose some strategies to the organization that helps in retaining the old customers and improve the profits.

Cluster Optimization for Boundary Points using Distributive Progressive Feature Selection Algorithm

“…This type of process is called include building. [6] In some cases usage the dimentionality reduction methods also. Some of the dimentionality reduction techniques are 1.…”