利用二型模糊聚类进行全球海表温度数据挖掘

基金项目: 国家973计划资助项目(2006CB701305)

详细信息

Global SST Data Mining Based on Fuzzy Clustering

¹ School of Remote Sensing and Information Engineering,Wuhan University,129 Luoyu Road,Wuhan 430079,China;² Center for Earth Observation and Digital Earth,Chinese Academy of Sciences,9 South Dengzhuang Road, Haidian District,Beijing 100094,China

Funds: 国家973计划资助项目(2006CB701305)

摘要: 基于二型模糊集的C均值聚类方法对全球时序海表温度数据进行了聚类分析,得到全球海表温度异常的典型聚类模式,并从聚类中心挖掘出潜在的海洋气候指数。
- 模糊聚类 /
- 海表温度 /
- 数据挖掘 /
- 海洋气候指数 /
- 气候变化
Abstract: This paper implements global SST clustering analysis using C-means clustering method based on type 2 fuzzy sets,from which the typical clustering patterns of SST anomaly are discovered,and the potential ocean climate indices are discovered from the clustering patterns.
- fuzzy clustering /
- SST /
- data mining /
- ocean climate indices /
- climate change