# Analysis of Trends in Mega-Sized Container Ships Using the K-Means Clustering Algorithm
## 4 Discussion  
Currently, the size of ships is increasing at a rapid pace [58]. Many experts in the field have recognized the importance of this development, and various studies on 30,000-TEU container ships have been conducted [2; 3; 4]. However, based on the LOA of the 30,000-TEU class container ship, the deviation of 83.9 m in the predicted range was observed from  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Terminal** & **Terminal Water** & **Time Windows of** & **MHHW** & **Terminal** & **Maximum** & **Required** \\
**Name** & **Depth (m)** & **High Tide** & **(m)** & **Water Depth** & **Draft of** & **Water Depth** \\  & & & **during High** & **25,000-TEU** & **(Maximum** \\  & & & **Tide (m)** & **Ship (m)** & **Draft \(\times\) 1.3)** \\ \hline Rotterdam & 20.0 & 3:03 p.m. (CEST) & 1.88 & 21.88 & 16.9 & 21.97 \\ Hongkong & 15.5 & 7:11 p.m. (HKT) & 1.73 & 17.23 & 16.9 & 21.97 \\ Shanghai & 15.0 & 10:48 p.m. (CST) & 2.03 & 17.03 & 16.9 & 21.97 \\ Singapore & 20.0 & 10:11 p.m. (UTC+8) & 2.28 & 22.28 & 16.9 & 21.97 \\ Busan & 20.0 & 6:56 p.m. (KST) & 1.03 & 21.03 & 16.9 & 21.97 \\ \hline \end{tabular}
\end{table}
Table 10: Terminal water depth at high tide and required water depth for 25,000-TEU container ship (Park and Suh [4]).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Classification** & **Mean** & **Standard** & **Coefficient of** & **Mean** & **Standard** & **Coefficient of** \\  & **Deviation** & **Variation** & **Variation** & **Deviation** & **Variation** \\ \hline Cluster 1 & 29.29 & 7.21 & 0.25 & 2.91 & 1.23 & 0.42 \\ \hline Cluster 2 & 9.13 & 3.02 & 0.33 & 1.71 & 1.76 & 1.03 \\ \hline Cluster 3 & 4.73 & 8.19 & 1.73 & 1.10 & 1.65 & 1.50 \\ \hline \end{tabular}
\end{table}
Table 9: Results of analysis on changes in length overall and breadth of ships according to 10,000.  
453.0 m to 536.9 m. Because changes in ship dimensions determine the physical requirements of port facilities and equipment, results of only simple linear regression analysis have limitations in predicting the trend of mega-sized ships. In this study, the standard design of container ships was evaluated through regression analysis, where \(k\)-means clustering was used to classify 5497 container ships in actual operation into groups. The results revealed that a 30,000-TEU container ship is predicted to have a LOA of 428.4 m, B of 67.6 m, and d of 17.0 m. Compared to the dimensions provided in previous studies, our obtained dimensions indicate a smaller LOA, larger B, and smaller d. Previous studies that suggested the specifications of large container ships only presented predicted values through linear regression analysis, targeting the ship's main dimensions of container ships. This study paid attention to the fact that the amount of change in the ship's main dimensions (LOA, Lpp, B, d) increasing varies according to the increase in the size of container ships. This research was able to obtain more accurate results that fit reality in that it analyzed clustered groups using the \(k\)-means clustering technique. However, because d was fixed to 17.0 m in this study, this result may change if the dredging of major container ports in the future is considered. Nevertheless, further research on ship dimensions can minimize the social cost of designing ports to accommodate the future enlargement of ships.