Deep Sea Research Part I: Oceanographic Research Papers
Deep Sea Research Part I: Oceanographic Research Papers
复制标题
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Y. Hisaki
中科院分区：
文献类型：
--
作者：
Y. Hisaki
海洋数据图的分类对于海洋数据的分析非常重要。在这里，我们将自组织映射 (SOM) 分析与 Ward 方法和 K 均值方法等聚类方法进行比较。使用日本冲绳岛以东的 HF（高频）雷达表面电流数据进行比较。观测区有两种典型的洋流模式：强南向洋流和顺时针涡状洋流。 Ward方法的分类结果与SOM分析的结果相似。 SOM 分析对用于降低数据维度和噪声的截止经验正交函数 (EOF) 模数不敏感，而 K 均值方法对 EOF 模数最敏感。
Classification of ocean data maps is important for analysis of ocean data. Here, we compare Self-Organizing Map (SOM) analysis with cluster methods such as the Ward method and K-means method. The HF (high-frequency) radar surface current data east of Okinawa Island, Japan were used for the comparison. There are two typical current patterns in the observation area: a strong southward current and a clockwise eddy-like current pattern. The classification results by the Ward method was similar to that by the SOM analysis. SOM analysis was insensitive to the cut-off Empirical Orthogonal Function (EOF) mode number for reducing the data dimensions and noise, while the K-means method was the most sensitive to the EOF mode number.