关键词: 船舶滞留缺陷, 关联规则, Apriori算法, 置信增强度

Abstract: To investigate the patterns of ship detention defects in Port State Control (PSC) inspections and reduce the probability of ship detention, this paper constructs an association rule analysis model for ship detention defects based on an improved Apriori algorithm, considering the characteristics of PSC inspections and ship attributes. Using the PSC inspection data of detained ships from the Tokyo Memorandum of Understanding (Tokyo MOU) between 2018 and 2023 as the research sample, the study first processes the ship attribute data by applying association rule mining technology to perform dimensionality reduction and discrete standardization, thereby forming effective samples for analysis. An improved Apriori algorithm, which incorporates constraints on both antecedent and consequent and introduces the evaluation metric of confidence boost, is employed to conduct an in-depth mining analysis of ship detention defects. Empirical results demonstrate that the improved Apriori algorithm can efficiently screen out significant strong association rules and accurately reveal the patterns of ship detention defects. These patterns can provide an important basis for ship safety risk management and help enhance navigation safety.

Key words: ship detention defects, association rules, Apriori algorithm, confidence boost