Aviation Passenger Segmentation through GANs Integrating K-Means Clustering: Innovating Airline Optimization

This study integrates K-means clustering and Gen-erative Adversarial Networks (GANs) to segment aviation pas-sengers. The paper leverages GAN-generated synthetic data to develop novel strategies for airline optimization. This approach aims to enhance passenger segmentation through computational analysis, thereby improving aviation safety, marketing effective-ness, satisfaction, loyalty, and operational efficiency in the airline industry.