计算机应用与软件2017，Vol.34Issue(11)：39-43,90,6.DOI:10.3969/j.issn.1000-386x.2017.11.007
海洋水文观测数据聚类
CLUSTERING OF MARINE HYDROLOGICAL OBSERVATION DATA
摘要
Abstract
In the course of scientific investigation,the acquisition of data is greatly affected by the natural environment factors and the monitoring cost.The actual number and location of monitoring points may not be able to meet the expectations and the collected data set usually contains a variety of monitoring elements.It is particularly important to use data analysis to compensate for lack of data caused by the natural environment and find out the law of data change.Based on the hydrological data of Prydz Bay in Antarctica,the use of spatial interpolation method to make up lack of data and sparse monitoring points,then the improved Dynamic time warping distance algorithm is applied to the similarity measure of hydrological depth series with multi-element.The experimental results show that similarity measurement algorithm is more accurate than the traditional Euclidean distance.Based on the similarity measurement proposed in this paper,the Prydz Bay hydrological data are clustered and the spatial distribution of each cluster is obtained.关键词
水文数据/空间插值/动态时间弯曲/相似度衡量/深度序列/K-meansKey words
Hydrological data/Spatial interpolation/Dynamic time warping/Similarity measure/Depth sequence/K-means分类
信息技术与安全科学引用本文复制引用
闫可,程文芳..海洋水文观测数据聚类[J].计算机应用与软件,2017,34(11):39-43,90,6.基金项目
极地海洋环境监测网系统研发及应用示范项目(201405031). （201405031）
