An Extended ID3 Decision Tree Algorithm for Spatial Data
Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data.It is because spatial data ming algorithms have to consider not only objects of interest itself but also neighbours of the objects inorder to extract useful and intersting patterns. One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial data set containing polygon features only. The objeciive of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. Asin the ID3algorithm that use information gain in the allribute selection, the proposed algorithm uses the spatial information gain to choose the best splitting layer from a set of explanatory layers. The new formula for spatial information gain is proposed using spatial measures for point,line and polygon features. Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing spatial decision tress on small spatial dataset. The proposed algorithm has been applied to the real spatial dataset consisting of point and polvgon features. the result is a spatial decision tree with 138 leaves and the accuracy is 74.72％.
ID3 algorithm spatial decision tree spatial information gain spatial relation spatial measure
Imas Sukaesih Sitanggang Razali Yaakob Norwati Mustapha Ahmad Ainuddin B nuruddin
Faculty of Computer Science and Information Technology,Universiti Putra Malayisa 43400 Serdang Selan Institute of Tropical Forest Products(INTROP),Universiti Putra Malaysia
国际会议
福州
英文
48-53
2011-06-29（万方平台首次上网日期，不代表论文的发表时间）