Entropy-Based Model for Geo-Data Mining
基于信息熵的地学空间数据挖掘模型

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.