Clustering Pengunjung Mall Menggunakan Metode K-Means dan Particle Swarm Optimization

This research aims to cluster mall visitors. is motivated by the mall's income which has decreased since pandemic. Later from these several clusters we can find out characteristics of Those will be used later increase mall. In this research, use a dataset Kaggle named Pengunjung_mall in CSV format processed using Python language on Jupiter Notebooks K-Means method. To ensure how accurate method is, optimization carried PSO (Particle Swarm Optimization) After performing clustering and Jupyter Notebook, results then evaluated with DBI (Davies Bouldin Index) Microsoft Excel well Clustering generated. The obtained are as reference determine visitors one strategy Mall profits. As result, have succeeded dividing customers into 5 based their annual earned expense scores. been optimized increasing resulting proven Davies Index concluded that who high levels spending scores targets highest priority level for malls.