Spatial Decision Tree: A Review

Abstract

The automated discovery or spatial information has leaded to widespread USC or spatial databases. Spatial data have been collected in vurious computer systems such as Geographical Information Systems (GIS). This foci leads lo an increasing interest in mining interesting and useful but implicil spatial patterns. Data mining refer!I to C\tracting or "'mining" I.no\\ ledge or patterns from a large number of data. Spatial data mining is a process to disco"cr ~patial patten\!> from huge amounts or spatial data. In the spatial data mining system, the attributes of the neighbors of an object may haven significant influence on the object itself. The research in spatial data mining has gnincd a high attraction due to the importance or its applications. Classification, '"hich is one of' the important tasks in data mining, has been used in lcnming process in order to cle"elop models (classifiers) from spatial data training. Then, the model can be used to predict the class or Ile\\ data. Decision tree induc1ion is the widely used method in classification tosks. Spatial decision trees refer to a mooel expressing clossification rules induced from spatial dala. ln this paper re\ic\\, we present some works in implementing a spatial data mining algorithm especially spatial decision tree algorithms.