关键词: 趋势函数, 空间数据, 聚类

Abstract: According to the neglect problem of the importance of attribute data while spatial data clustering and the tendency exploration of spatial feature data, a trend function is established for describing the attribute value change with spatial location. Then, second-order model is constructed by referencing to the variation function. In view of the above, a similarity function integrated spatial distance and attribute difference is built. A treatment scheme regarding of angle tolerance is discussed under stationary hypothesis. Finally, a cluster model named K-Trend is set up with taking trend function as a core. The results show that the K-Trend cluster method has high quality, is seldom affected by sample size, and has moderate time-consuming. All of these features improve the practicability of spatial data cluster.

Key words: trend function, spatial data, cluster