A Visual Cluster Map Technique for Improving Air Transportation Business across the World over Existing Clustering and Visualization Methods

Visual methods are more effective than written ones. In fact, legacy visualization methods deceive the users or misrepresent the information when providing datasets for visualization. Furthermore, if users are required to see the top-down performance of the air freight and airline route systems, the existing clustering and visualization techniques will not provide significant results. Moreover, the visual map tools could visualize the mixed data types regarding regions. However, the visual map tools do not verify the authentication between the map tool’s attributes and input data. The VOSviewer and Gephi tools create cluster networks; however, they are unable to integrate multiple variables between sources and destinations within a network. So, it provides visibility, making it hard and difficult to make critical decisions. Therefore, it is one of the significant challenges to bring together multiple features between the sources and targets regarding their similarity in the visual clustering format. However, the proposed method combines multiple features between edges and nodes based on their similarity and provides transparent information about the air transportation business as compared to VOSviewer and Gephi tools. The proposed method generates consistent data clusters in a star format, particularly when compared to k means, orthogonal clustering (OC), and two-step clustering methods. The findings suggest that the proposed method prepares consistent data clusters in a database and visualizes multiple features between edges and nodes in a star format. Finally, the proposed method is devoid of both intrinsic and extrinsic threats to validation and free from outliers.