Comparison of K-Means, Affinity Clustering, and Mini Batch K-Means Algorithms for Market Segmentation Analysis

Abstract

Market segmentation is the process of dividing a market into homogeneous groups of buyers based on certain characteristics. Market segmentation is important for businesses to understand the needs and behaviors of their customers so that they can develop more effective marketing strategies.This study compares three clustering methods, namely K-Means Clustering, Affinity Propagation Clustering, and Mini Batch K-Means, in the context of market segmentation analysis. The data used is the marketing_campaign.csv dataset consisting of 29 columns and 2240 rows. Experiments were conducted to evaluate the performance of the three methods using the silhouette metric. The results of the experiments showed that Affinity Propagation produced the highest silhouette value of 0.5861, followed by K-Means with 0.4675, and Mini Batch K-Means with 0.4659. The finding indicates that Affinity Propagation can be a good choice for market segmentation analysis on that dataset. The results of this comparison provide insights into the strengths and weaknesses of each clustering method, helping users choose the approach that is most appropriate for their analytical goals. The study contributes to the practical understanding of the application of clustering techniques in the context of marketing.