Performance Comparison of K-Means and DBSCAN Methods for Airline Customer Segmentation

Abstract

Organizations are now fully embracing ideas such as customer success, customer loyalty, customer experience management and customer satisfaction. The application of these concepts must be based on three pillars of technology, process and people, to ensure that the organization ultimately has satisfied, loyal and successful customers. In today's competitive environment, as in all sectors, gaining great services in the aviation industry can provide a competitive advantage. With this study, it is aimed to help aviation companies to know how their services should meet the needs of customers and to obtain passenger satisfaction. Customer segmentation is widely used, which groups objects according to the similarity difference on each object and provides a high level of homogeneity in the same cluster or a high level of heterogeneity between each group. The aim of this study is to examine airline passenger satisfaction by using data mining methods including K-Means and Density-based spatial clustering of applications with noise (DBSCAN) clustering algorithms to reveal the service quality importance for customer satisfaction. K-Means algorithm achieved slightly better results than DBSCAN algorithm with a Silhouette value of 0.1450671.

Keywords

• Clustering
• Customer segmentation
• K-means
• DBSCAN
• Data mining
• Data management

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