An Optimized Hybrid Clustering Algorithm for Mixed Data: Application to Customer Segmentation of Table Grapes in China

Customer segmentation based on mixed variables is an important research direction of current market segmentation methods. However, using the hybrid clustering method to divide consumer groups will overestimate the clustering contribution of categorical variables and shield numerical variables that have important marketing significance. In this study, we improve the hybrid clustering algorithm and design an indicator of assessing the customer groups’ differences. Firstly, the coefficient of Features Discrepancy between Segments (FDS) and three variable weighting strategies are designed. Then, the optimal optimization scheme is determined based on three hybrid clustering algorithms and evaluation indicators. The results show that the variability weighting method can effectively improve the problem that categorical variables dominate hybrid clustering. The clustering performance and stability of PAM method are the best among the three hybrid clustering algorithms. Finally, clustering the consumer consumption values and the basic population information through the weighted PAM method to verify this method’s effectiveness. This study provides a practical application value for the improvement of existing technologies in customer segmentation methods. It also offers the marketing suggestions for the table grape operators.