The following is a link to the R scripts which generate the figures used in the ICDM’2010 (to-be-reviewed) paper. The functions for computing the root mean squared error are in 20-*R and 21-*R, where the first is for the non-spatial case and the second is for the spatial analysis, including clustering (which is a one-liner in R, just as many other things). The relevant functions are NonSpatialRegression() and spatialPredictionWithClustering(). The scripts might not be of much use without the data sets, but they may be tailored easily to other data sets. Should you have questions, feel free to drop me a few lines, I’m happy to answer. You might also consider participating in my workshop on Data Mining in Agriculture (DMA’2010).

Link: Rscripts-icdm2010.tar

The following is a paper summary for the ICDM 2010 conference, which will be held in Berlin during July. It mainly elaborates on the issue of spatial autocorrelation in the agriculture data I’m using. It refers to my previous publications (2008, 2009) at this conference where I presented standard regression approaches using different techniques for the task of yield prediction. It seems these techniques considerably underestimate the prediction error due to spatial autocorrelation. I therefore developed an approach based on k-means clustering to enable yield prediction on spatial data sets. The conference reports from the previous years are here: , 2008, 2009.
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