Association rule mining for identification of port state control patterns in Malaysian ports. Issue 8 (17th November 2021)

• Record Type: Journal Article
• Authors: Osman, Mohd Tarmizi; Yuli, Chen; Li, Tian; Senin, Syahrul Fithry
• Is Part Of: Maritime policy & management. Volume 48:Issue 8(2021)
• Page Start: 1082
• Page End: 1095
• Publication Date: 2021-11-17
• Subjects: Port State Control -- data mining -- association rule -- Apriori algorithm -- Malaysia; Marine resources -- Periodicals; Shipping -- Management -- Periodicals; Policy sciences -- Periodicals; 333.9164
• DOI: 10.1080/03088839.2020.1825854
• Languages: English
• ISSNs: 0308-8839

Abstract

ABSTRACT: Port State Control (PSC) inspection data is used for determining the inspection pattern of PSC in Malaysia and identifying the relationship between the inspection place, flag state, number of deficiency, detention result, and ship risk profile. Based on 8, 089 inspection reports from 2015 to 2019, the mining association rule is proposed as a learning approach due to its determination pattern in the information bank. The learning of association rules of PSC inspections is performed primarily on the Apriori Algorithm, in order to produce alluring rules. Inspection patterns of Malaysian ports revealed that flag state, ship risk profile, and inspection place generally lead to no detention result, as well as zero deficiency recorded on-board. The reported quantity of detention was significantly related to the high number of deficiencies raised for ships registered under blacklisted countries. Furthermore, the analysis of deficiency discovered the pattern of inspection at Malaysian ports is frequently related to zero and a low number of deficiencies raised by inspectors. Lastly, five major ports were selected for providing a useful rule to help PSC officers in organising an effective inspection plan. A similar approach can also be used for other ports beyond Malaysia for comparative analysis.