Integration of Artificial Immune System and K-Means Algorithm for Customer Clustering

This study proposes a novel artificial immune system (AIS)-based clustering algorithm, which integrates with a K-means (AISK) algorithm for a customer clustering problem. Computational results using Iris, Glass, Wine, and Breast Cancer benchmark datasets indicate that the proposed AIS-based clustering algorithm is more accurate than some particle swarm optimization (PSO)-based clustering algorithms. In addition, the model evaluation results using a daily transaction database provided by a cyberstore also show that the proposed AISK algorithm is superior to PSO-based clustering algorithms.