Just a quick post that aims to make tomorrow’s slides for my IPMU 2010 talk available. It’s going to be about the management of spatial information and especially the issues which
arise when using non-spatial models on spatial data.

Slides link: russ2010ipmu-slides.pdf

As usual, the paper is in our publication database: Data Mining in Precision Agriculture: Management of Spatial Information.

I’ve had a whole lot of fun writing a paper for the IEEE ICDM conference, which
is going to take place in Sydney, Australia, this year. The programming work
was there, I had some novel data sets to analyse and I came to some cool
conclusions using my homebrew algorithm which explicitly assumes spatial
autocorrelation in the data sets. I could also show that the algorithm produces
meaningless results when spatial autocorrelation does not exist.

It also implements a more or less standard hierarchical agglomerative
clustering procedure on spatial data — there just was no existing work which
fit the problem and the data set, so I had to create my own algorithm using a
straightforward and easily explainable divide-and-conquer approach. I hope that
my reviewers at the IEEE ICDM conference like the idea.

I’m still looking for an easily pronouncable acronym, maybe HACSAD-PA
will do: hierarchical agglomerative clustering for spatially autocorrelated
data from precision agriculture :-)

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Yesterday I was informed that I’ve been given the best graduate student paper award at the International Conference on Precision Agriculture 2010, which is something like the flagship conference in precision agriculture, much like the IEEE ICDM (held in Sydney, Australia, this year) or the PKDD (held in Barcelona, Spain, this year) are for data mining and knowledge discovery in databases. I had to be nominated for this award and parts of the nomination included my vision on the area of precision agriculture. This is quoted below:
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