利用Apriori算法进行船舶缺陷数据研究分析  

Research and analysis of ship defect data using Apriori algorithm

作　　者：张晓宁[1] 李娜[1] 朱方娥[1] 

机构地区：[1]石家庄铁道大学四方学院计算机系,河北石家庄050000

出　　处：《舰船科学技术》2016年第14期139-141,共3页Ship Science and Technology

摘　　要：港口国检查是以确保人员和船舶安全、预防海事事故、防止海洋环境污染为目的的专门检查。随着环保意识的不断加强,港口国检查发挥着越来越重要的作用。数据挖掘技术能够从大量模糊数据中发现有价值的信息。船舶缺陷数据分析利用数据挖掘技术,从海量船舶数据中发现数据间的关联规则,从而提高港口国检查的工作效率。本文研究Apriori算法,针对该算法的不足进行改进,实现了基于改进Apriori算法的船舶缺陷数据分析系统。PSC is a special inspection to ensure the safety of the ship,and the prevention of maritime accidents,to prevent pollution of the marine environment for the purpose of. With the increasing awareness of environmental protection,PSC plays an increasingly important role. Data mining technology can find valuable information from a large number of fuzzy data. The defect data analysis using data mining technology,association rules from data between the massive data in the ship,so as to improve the work efficiency of port state inspection. This paper studied the Apriori algorithm,and improved the defects of this algorithm,and realized the ship defect data analysis system based on the improved Apriori algorithm.

关 键 词：关联规则 船舶缺陷 数据分析 

分 类 号：U691.6[交通运输工程—港口、海岸及近海工程]