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It is impossible to create model of decision process, as we know nothing about the original decision process. Although it is possible to build models that can get us to the spaces where our fitness is strong enough. These models can contain hard data and soft information as well. In the background of the widely accepted solutions there are transformations of soft information into hard data. This leads us to the world of quantitative decision support. This step is very dangerous! The decision maker uses logic not arithmetic in his thinking process. DoctuS© Knowledge-Based System uses logic. The latest version is also capable of data mining. Using a clusteranalyzing algorithm it can transform the relations between hard data into soft information, which will be used for deduction in reasoning. The number of clusters is given by the user. The cluster-analyzing algorithm makes the clusters using learning example. When running the data mining the clusters remains unchanged and the new data will be transformed. The clusters can be handled using logic. For illustration we use an example of taking decision about location for a power plant.
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