Work

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

Year: 2024

Type: article

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.

Source: Electronics

DOI: 10.3390/electronics13173523

Authors: Liqun Xiao, Jiashu Zhang

Institution: Southwest Jiaotong University

Language: English

Topic: Customer churn and segmentation

Subfield: Marketing

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funders: National Science Foundation, National Natural Science Foundation of China