For the SGAI-2008 conference paper it is required that the figures are in black-and-white. I can’t just convert the color figures into b/w because the color maps are wrecked and dark parts in SOMs that were clearly distinguishable before are the same shade of black after the conversion. The som_show function in the SOM toolbox has a colormap parameter. As it turned out, as usual, there’s just one more thing to do to generate the figures in a gray-scale colormap, using Matlab’s colormap function:

som_show(sM, [... more parameters ...],'colormap',colormap(gray))

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When generating some figures for a paper, I was looking for an easy way to save figures the way I wanted them. There are some limitations within Matlab’s built-in functions for that. My solution is to use the savefig script by Peder Axensten, which is available from the Mathworks‘ site at Matlab Central. It may save some time for you, too. Peder also has authored further (probably useful) scripts.

The technical report that I linked to in the latest article is really comprehensive. Together with the supplied iris dataset (that can, of course, also be obtained elsewhere), the SOM toolbox works well and out-of-the-box, as expected. Seems as if the agricultural yield data we have are really interesting and can be visualised appealingly. The first labeled map shows the clustering capability of the SOM. There have been two fertilization strategies on the field where the data come from, simply named „B“ and „N“ here. The second map shows how much fertilizer has been used on the field, from low (blue) to high (red) values. The correlation between the labels and the colored map is obvious. Nevertheless, the ultimate goal still is to try to outperform a neural network in predicting the current year’s yield from sensor data and historical data and, along the way, identify indicators of a field’s heterogeneity.

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It turned out that Vesanto’s SOM toolbox for Matlab is very well written and works right out-of-the-(tool)box. It’s advisable to read the documentation and the technical report (.pdf). And it’s really worthwile to try one of the matlab examples which is some kind of tutorial on how to work with the SOMs.
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I feel as if I had just returned from AI-2007 (at least the expenses were paid just recently by the University) and there’s the deadline approaching for AI-2008, again held at Peterhouse College, UK.
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Scientific American just posted an interesting article on Science 2.0, which is namely describing the use of Web2.0-techniques in scientific work. Well, I’ve somehow done that transition already with this blog. There’s also, for larger research groups, the wiki technique to enable closer collaboration. There have been earlier attempts from the MIT, called Open WetWare, which is used to

promote the sharing of information, know-how, and wisdom among researchers and groups who are working in biology & biological engineering.

According to the SciAm article, people publish almost all of their work in progress online, like How-Tos, intermediate results from experiments, publication discussion and so on. This is also my philosophy of working in research. It’s not publish or perish but to get into discussion or high-level arguments, receiving early feedback and promoting useful ideas and hints to other researchers.

At the moment I’m still collecting ideas for data mining of agricultural data. It looks as if it’s going to be research and experimentation with supervised learning methods on those data and, in the process, describing the data flow from the wheat field to the machine learning task and back to the field. The next paper deadline is for the AI-2008, again held at Peterhouse College, Cambridge, UK. I’ll have to sort out the content first, though.

As recent as of March 28th, another paper of my/our work on the agriculture data got accepted. The conference is the Industrial Conference on Data Mining 2008, which will be taking place not far away in Leipzig, Germany. Based on the results and the information from those conferences, I might try to submit advanced work at BCS AI-2008, which will again take place at (frosty) Peterhouse College in Cambridge, UK. I won’t be there for long, though, as offspring is already on his/her way.

Two papers of mine have been accepted recently. Both describe the process of data mining with neural networks for agriculture data. Therefore I’m quite confident that  this will be my PhD thesis‘ major point of interest. The two conferences, which also provided worthwile and in-depth reviews, are IPMU 2008, in Málaga, Spain and IFIP AI 2008 in Milano, Italy. Of course, papers have to be redacted and my presentation will have to be prepared, but it’s encouraging nevertheless.

I’m back from a research trip to Melbourne, Australia, that emanated from a successful grant application with the DAAD/GO8 under their cooperation scheme. Mine was the first trip to make and I visited the research group headed by Saman Halgamuge at the University of Melbourne. Actually, I had been working with them during my undergraduate studies for half a year in (southern hemisphere) summer 2004/2005. We made some arrangements concerning the exchange of students and research staff for 2008/2009 and my next visit is about to take place in March 2009. Of course, we (i.e. Mimi and I) also did some sightseeing and bushwalking in/on nearby Tasmania — I’m fond of that island, and it was the second time that I’ve been there. However, the time lag is ten hours at the moment, not to mention the delta T of around 30K between Magdeburg and Melbourne.

Having returned on Monday, I went back to work on Wednesday because I definitely wanted to see/hear Christian Borgelt’s lecture on Frequent Pattern Mining. The topic is quite interesting and the subtopic of Frequent Graph Mining, applied to chemical molecules, really has been worthwile to me since I’ve always been very interested in chemistry and also had chosen it as my subsidiary subject during my undergraduate studies.

There are quite a few deadlines for publications approaching in January and I will submit another paper detailing some of the recent accomplishments on the agriculture data there. One of the conferences is ICDM 2008, held in Leipzig, Germany. It targets industrial applications of data mining and I felt the paper fit in there quite nicely.

I have also received more data sets from Martin Schneider at Martin-Luther-University of Halle-Wittenberg which will have to be mined. There are quite a lot of interesting tasks to be performed on those data — that requires thorough planning. I probably won’t be able to do that planning until my return from the organizational business trip to Melbourne, starting a cooperation project between our research group and the one that I worked with in 2004/2005.

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