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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