Last week I also participated in the MLDM 2009, which is a biennial conference for Machine Learning and Data Mining, organised by the same team as the ICDM series. My paper was accepted as a poster presentation and I also chaired a session on association rules, which happens to be strongly related to my diploma thesis. The conference was a bit larger than the ICDM, with around 60 scheduled talks, of which 48 took place due to dropouts. It was a bit more theoretical than the ICDM, but still really worth it since usually the data mining problems were closely motivated by real-world problems.
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As I mentioned some time ago, I got a paper accepted at the ICDM 2009 conference, held in Leipzig, Germany. I really liked this small type of conference last year and it was even better this year. The organisers had scheduled 32 presentations in three days, no parallel sessions and 25 minutes of talking time for every presenting author. At least from my point of view, this conference was very useful since it wasn’t that much about the theory of data mining or machine learning, but focused instead on the practical point of view. There were lots of industry people who just had their data problems and applied data mining to it. Theory is important, but practical applications are what makes the world go round. The invited talks by Claus Weihs and Andrea Ahlemeyer-Stubbe were really good examples of theory and practice. Claus Weihs could even remember that he had seen my data mining problem before, at the IFCS 2009 in Dresden, where there were a lot more presentations than at the ICDM.
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This week saw me busy preparing for next week’s two conferences ICDM2009 and MLDM2009, both taking place at the same location in Leipzig consecutively.
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The paper which I mentioned in the previous post has been accepted for publication at the SGAI AI-2009 conference. The reviewers were rather confident about the paper contents and it seems that my work is quite interesting for computer scientists.

Nevertheless, I’ve started digging somewhat deeper into the issue with spatial autocorrelation which is likely to exist in the georeferenced data sets I’m using. So far, this has usually been neglected and might lead to biased results when regression is carried out. My main idea for my PhD contribution is to develop or find a regression model which does take the spatial autocorrelation into account.

To give you an idea of the data sets and fields I’m working with, here’s a georeferenced plot of the N2 fertilizer on one of the fields during 2007:

N2 dressing on one of the fields in 2007