Market Segmentation Using a PSO-Based Multivariate Fuzzy Weighted Fuzzy K -Modes Algorithm with Probabilistic Distance

This study introduces a new clustering method based on the multivariate fuzzy K-modes algorithm. The proposed algorithm incorporates attribute weights determined by Gini impurity, which evaluates the significance of attribute values in both within-cluster and between-cluster variances. Additionally, instead of relying on the Hamming distance, the probabilistic distance is employed to compute the dissimilarity between objects or between objects and their corresponding centroids. This study also u