Gms-Afkmc2: A New Customer Segmentation Framework Based on the Gaussian Mixture Model and ASSUMPTION-FREE K-MC2

Abstract

In this paper, the impact of initial clusters on the stability of customer segmentation methods based on K-means is investigated. We propose a novel customer segmentation framework, Gms-Afkmc2, based on the Gaussian mixture model and ASSUMPTION-FREE K-MC2, a better cluster-based K-means method, to obtain greater customer segmentation by generating better initial clusters. Firstly, a dataset sampling method based on the Gaussian mixture model is designed to generate a sample dataset of custom size. Secondly, a data clustering approach based on ASSUMPTION-FREE K-MC2 is presented to produce initialized clusters with the proposed dataset. Thirdly, the enhanced ASSUMPTION-FREE K-MC2 is utilized to obtain the final customer segmentation on the original dataset with the initialized clusters from the previous stage. In addition, we conduct a series of experiments, and the result shows the effectiveness of Gms-Afkmc2.