A Hybrid Machine Learning Model for Market Clustering

Abstract

Market clustering is increasingly important for companies to understand consumer shopping behavior in the context of complex data. This study aims to develop a hybrid model that integrates Principal Component Analysis (PCA) and k-medoids to enhance market clustering based on consumer shopping patterns. The methods used include data preprocessing, PCA application for dimensionality reduction, and clustering using k-medoids. The quality of the clusters is evaluated with various validity indices. The results show that the hybrid model produces clusters with better quality compared to the single k-medoids method, as seen from the Calinski-Harabasz Index (CHI), theSilhouette Width (SW), and the Davies-Bouldin (DB) index. The implications of these findings emphasize the importance of adopting hybrid methods in marketing strategies to improve understanding of consumer behavior dynamics and allow companies to adjust their marketing strategies more effectively. This study provides a strong foundation for further development in clustering analysis across various industry sectors and highlights the potential for innovative techniques to address dynamic market challenges.