中文摘要：

空间分类和预测是空间数据挖掘中一个非常重要的方法,但对它们的研究目前尚处于初始阶段.通过引入空间对象对模糊聚类的模糊隶属度的概念,提出了基于模糊c-means算法的空间数据分类和预测的方法（SFCM）.该方法首先用模糊c-means方法对数据集论域空间进行聚类,但由于空间数据具有空间自相关的特性,在用模糊c-means算法进行空间聚类时加入了空间信息.然后计算每个空间对象对所有聚类的模糊隶属度并从中找出模糊隶属度最大的聚类.最后用该聚类中心对象的因变量的值作为该空间对象的因变量的估计值.理论分析和实验结果表明,该算法是有效可行的.

英文摘要：

Spatial classification and predication is one of the very important spatial data mining techniques,but the present research work on them is still in their initial stage.In this paper,a spatial classification and prediction algorithm based on fuzzy c-means（SFCM） is proposed by introducing the concept of fuzzy membership degree of a spatial object to a fuzzy cluster.Firstly,this algorithm clusters the dataset by fuzzy c-means,spatial information must be added into the fuzzy c-means algorithm for spatial clustering due to spatial autocorrelation of spatial data.Secondly,it computer the fuzzy membership degree of each spatial object to all fuzzy clusters and finds the cluster that its fuzzy membership degree is the maximal.Finally,the dependent variable value of the spatial object is estimated by the dependent variable value of the mean object of the cluster.Theoretic analysis and experimental results show that SFCM is effective and efficient.