Data Analysis of Vessel Traffic Flow Using Clustering Algorithms

摘要：

An unsupervised machine learning method clustering, is introduced to conclude characteristics of vessel traffic flow data. A new way is found to implement data analysis in vessel traffic field using artificial intelligent technique. A similarity based algorithm, K-Means, is selected in the clustering process for its simplicity and efficiency and a popular data mining tool named WEKA is chosen to execute the experiment. The result of the data mining experiment, which use the real data from an water way of Yangzi river, list the most related cluster centroids and related explanations, which show us the fact often be neglected. A conclusion that clustering is a suitable method to generalize multi-factor related regulations is made finally according to the mining result and its reasonable explanation.

作者: ZHENG Bin CHEN Jinbiao XIA Shaosheng JIN Yongxing

作者单位: Shanghai Maritime University, Shanghai, China

会议类型: 国际会议

会议名称: International Conference on Intelligent Computation Technology and Automation(2008 智能计算技术与自动化国际会议 ICICTA 2008)

会议地点: 长沙

会议语种:英文

页码: 1534-1537

在线出版日期: 2008-10-20（万方平台首次上网日期，不代表论文的发表时间）