Visual Data Mining of Agriculture Data.
Georg Ruß, Rudolf Kruse, Martin Schneider, Peter Wagner · 2009
interwoven. The former usually refers to the application of nowadays ’ technol-ogy to agriculture. Due to the use of sensors and GPS technology, in today’s agri-culture many data are collected. Making use of those data via IT often leads to dramatic improvements in efficiency. For this purpose, the challenge is to change these raw data into useful information. Techniques or methods are required which use those data to their full extent – clearly being a data mining task. This paper presents experimental results on real and recent agriculture data that aid in the first part of the data mining process: understanding and visualizing the data. Self-organizing maps and multidimensional scaling techniques will be used to reduce the high-dimensional input data to two dimensions. The processed data can then be visualized appropriately on 2D maps. An analysis of correlations and interde-pendencies in the data set will be given, based on the visualization.