The article compares two types of clustering methods, hierarchical and partitional, to see how well they group customers based on their features. The researchers used data mining techniques to analyze a dataset of mall customers. They found that partitional clustering algorithms like k-means and PAM create clusters with maximum similarity within them and minimum similarity between them. On the other hand, hierarchical clustering algorithms like AGNES and DIANA group customers based on similar features. The researchers used the R programming language to apply these algorithms and interpret the results.