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Conclusions and Future Work

  • Baris M. KazarEmail author
  • Mete Celik
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

Linear regression is one of the best-known classical data mining techniques. However, it makes the assumption of independent identical distribution (i.i.d.) in learning data samples, which does not work well for geo-spatial data, which is often characterized by spatial autocorrelation. In the SAR model, spatial dependencies within data are taken care of by the autocorrelation term, and the linear regression model thus becomes a spatial autoregression model.

Keywords

Spatial Dependency Linear Regression Model Geospatial Data Error Ranking General Purpose Computer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© The Author(s) 2012

Authors and Affiliations

  1. 1.Oracle America Inc.NashuaUSA
  2. 2.Erciyes UniversityKayseriTurkey

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