An Efficient Approach for Search Using Spatial Decision Tree

Author(s):

A.Valarmathi , Shrimati Indira Gandhi College, Trichy; S.Geetha , Shrimati Indira Gandhi College, Trichy

Keywords:

Query processing, nearest neighbor, spatial data mining, focal-test-based spatial decision tree

Abstract:

A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes. Given a raster spatial framework, as well as training and test sets, the spatial decision tree learning (SDTL) problem aims to minimize classification errors. The SDTL problem has many applications. In the field of remote sensing, a large amount of images of the earth surface are collected. SDTL can be used to classify remote sensing images into different land cover types. Related work relies on local tests and cannot adequately model the spatial autocorrelation effect, resulting in salt-and-pepper noise. A focal-test-based spatial decision tree (FTSDT), in which the tree traversal direction of a sample is based on both local and focal (neighborhood) information is proposed in this paper. The experimental results show that the proposed system outperforms the existing methods.

Other Details:

Manuscript Id	:	IJSTEV3I1032
Published in	:	Volume : 3, Issue : 1
Publication Date	:	01/08/2016
Page(s)	:	36-39

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