| 基于FP-tree算法的船舶滞留原因关联性分析 |
| 投稿时间：2014-11-22 修订日期：2015-03-16 点此下载全文 |
| 引用本文：顾洵瑜,胡甚平,吴建军,陈兴伟.基于FP-tree算法的船舶滞留原因关联性分析[J].上海海事大学学报,2015,36(2):60-64. |
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| 基金项目:浙江海事局项目（201425） |
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| 中文摘要:为提高船舶安全检查的效率，提出对港口国监督(Port State Control,PSC)中船舶安全检查要素之间关联性的研究.引入关联规则进行相关性分析，从给定的数据中寻找频繁的项目知识模式，通过置信度和重要性阈值的约束，挖掘出船舶滞留原因间的潜在规律.算例结果表明，通过关联规则对船舶滞留原因的分析，可以直观地发现滞留原因间的关联，利于港口主管机关在实际工作中采取更具针对性的方法进行检查. |
| 中文关键词:港口国监督(PSC) 数据挖掘 关联规则 滞留 缺陷 |
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| Correlation analysis of ship detention reasons based on FP-tree algorithm |
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| Abstract:To improve the efficiency of ship safety inspection, the correlations among ship safety inspection elements of Port State Control are studied. By introducing association rules to analyze the correlations, the frequent project knowledge models are found out from the given data. Then through the constraints of confidence and importance threshold values, the potential laws in ship detention reasons are mined. The result from a case shows that the correlations among ship detention reasons can be directly found out through the association rule analysis, which helps port authorities to take more effective measures in the practical work. |
| keywords:Port State Control (PSC) data mining association rule detention deficiency |
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