Abstract
Global seaport network efficiency can be measured using the Liner Shipping Connectivity Index (LSCI) with Gross Domestic Product. This paper utilizes k-means and hierarchical strategies by leveraging the results obtained from Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) to cluster 133 countries based on their seaport network efficiency scores. Previous studies have explored hkmeans clustering for traffic, maritime transportation management, swarm optimization, vessel trajectory prediction, vessels behaviours, vehicular ad hoc network etc. However, there remains a notable absence of clustering research specifically addressing the efficiency of global seaport networks. This research proposed hkmeans as the best strategy for the seaport network efficiency clustering where our four newly founded clusters; low connectivity (LC), medium connectivity (MC), high connectivity (HC) and very high connectivity (VHC) are new applications in the field. Using the hkmeans algorithm, 24 countries have been clustered under LC, 47 countries under MC, 40 countries under HC and 22 countries under VHC. With and without a fuzzy dataset distribution, this demonstrates that the hkmeans clustering is consistent and practical to form grouping of general data types. The findings of this research can be useful for researchers, authorities, practitioners and investors in guiding their future analysis, decision and policy makings involving data grouping and prediction especially in the maritime economy and transportation industry.
Funder
Ministry of Higher Education, Malaysia
Publisher
Public Library of Science (PLoS)
Reference38 articles.
1. Big data analytics and machine learning of harbour craft vessels to achieve fuel efficiency: a review;Z. Y. Tay;J. Mar. Sci,2021
2. A survey on k-mean clustering and particle swarm optimization;P. Vora;Int. J. Mod. Sci. Eng. Technol,2013
3. Improve K-mean clustering algorithm in large-scale data for accuracy improvement;M. Dhamecha;Machine Intelligence and Soft Computing; Springer: Singapore,2021
4. Application of improved multi objective ant colony optimization algorithm in ship weather routing;G. Zhang;J. Ocean Univ. China,2021
5. A new clustering method based on the inversion formula;M. Lukauskas;Math,2022