Spatial Classification and Prediction Based on Fuzzy cmeans

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.