Clustering Customer Data Using Fuzzy C-Means Algorithm

Abstract

The pattern of the service industry is influenced mostly by economic growth. When economic growth rises, the economic activity will also grow as in the case of insurance activities. One of the assets owned by an insurance company is the customer, hence the existence of a loyal or potential customer should be maintained by the insurance company. This study focuses on clustering or grouping the existing customer data in insurance companies using the Fuzzy C-Means (FCM) algorithm. This study uses data from the company for analysis and the results can be used as a basis for insurance companies in making decisions, especially those related to further insurance marketing to customers who have participated in insurance or who are still actively registered in payment insurance. Fuzzy C-Means can be used for clustering the customer datasets. It obtained 3 clustering results using Partition Coefficient (PC) in determining the validity index and the centers value was ranged from 0.5 to 1.0.

Feature Selection on Grouping Students Into Lab Specializations for the Final Project Using Fuzzy C-Means

“…Filling in empty values with the average grade per course, the example can be seen in Figure 3. FCM uses a fuzzy clustering model so that data can be a member of all classes or clusters formed with different degrees or membership levels between 0 and The level of data presence in a class or cluster is determined by its membership degree [5].…”