摘要
为了提高海洋漂流浮标观测数据的质量,提出一种新的基于兴趣度模型的关联规则挖掘算法。通过该关联规则算法挖掘浮标观测数据,提取出所有关联项对形成范例库,以此构建海洋漂流浮标数据质量控制模型,并与传统数据质量控制方法对比,发现基于新的关联规则算法的质量控制模型在检出率和灵敏度以及性能方面有极大提高,非常具有可行性。通过真实数据验证表明,新算法不仅能够挖掘出所有相关性很强的规则,与同类非Apriori类算法相比,在时间性能上更加优越。
A new association rule mining algorithm based on the interestingness model is proposed to improve the observa-tion data quality of the ocean drifting buoy.The association rule algorithm is adopted to mine the buoy observation data,so as to extract all the correlation pairs to form a sample database,based on which the data quality control model of the ocean drifting buoy is constructed.By comparing with the traditional data quality control methods,it is found that the quality control model based on the new association rule algorithm has improved a lot in detection rate,sensitivity and performance,which is of great feasibility.The results of the real data verification show that the new algorithm can mine all rules with strong correlation,and has more superior time performance than other non-Apriori algorithms of the same class.
作者
李涛
张灿
张帅弛
陆正邦
LI Tao;ZHANG Can;ZHANG Shuaichi;LU Zhengbang(School of Electronic&Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China)
出处
《现代电子技术》
北大核心
2018年第22期138-142,共5页
Modern Electronics Technique
基金
公益性行业(气象)科研专项(GYHY201306070)
江苏高校品牌专业建设工程自助项目(PPZY2015B134)
江苏省高等学校大学生创新创业训练计划项目(201610300031)~~
关键词
海洋漂流浮标
兴趣度
关联规则
挖掘算法
气象数据
质量控制
ocean drifting buoy
interestingness
association rule
mining algorithm
meteorological data
quality control
