基于信息熵的地学空间数据挖掘模型

从信息熵的基本概念出发，认为地学空间数据子集划分产生的互信息或熵减源于子集划分，使得各个子集的不确定性或模型糊降低，并且子集之间的差异性增在 最大熵减的子集划分方案代表一定的地学模式和地不规律。以此为基础分别探讨了地学数据属性要素的子集划分产生多维属性关联规则，以 间和时间的子集分割来进行了聚类的方法。

Entropy-Based Model for Geo-Data Mining

More and more interest has been paid on Geo|data mining and knowledge discovery from large database with the rapid growth of Geo|data volume and eagerness for the Geo|knowledge. This paper presents a data|set partition model based on information entropy and mutual information. The author argued that the largest information entropy deduction is in accordance with the significant Geo|data pattern. With this kernel theoretical base, information|entropy|based decision|tree model and spatial|temopral clustering by partition model were developed.