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利用二型模糊聚类进行全球海表温度数据挖掘 被引量：6

Global SST Data Mining Based on Fuzzy Clustering
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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。 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.
作者 孔令桥 秦昆 龙腾飞
机构地区 武汉大学遥感信息工程学院 中国科学院对地观测与数字地球科学中心
出处 《武汉大学学报（信息科学版）》 EI CSCD 北大核心 2012年第2期215-219,255-256,共5页 Geomatics and Information Science of Wuhan University
基金 国家973计划资助项目(2006CB701305)
关键词 模糊聚类 海表温度 数据挖掘 海洋气候指数 气候变化 fuzzy clustering SST data mining ocean climate indices climate change
分类号 P208 [天文地球—地图制图学与地理信息工程]
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参考文献12

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二级参考文献57

共引文献296

同被引文献40

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引证文献6

二级引证文献16

武汉大学学报（信息科学版）
2012年 第2期

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