KnowledgeMiner is a data mining tool that enables anyone to use its unique form of modeling to quickly visualize new possibilities. It is an artificial intelligence tool designed to easily extract hidden knowledge from data. It was built on the cybernetic principles of self-organization: Learning a completely unknown relationship between output and input of any given system in an evolutionary way from a very simple organization to an optimally complex one. The main advantages of the inductive KnowledgeMiner approach are: Only minimal, uncertain a priori information about the system is required That means, even if you have no experience in modeling, data analysis or designing a neural network you will be able to model, analyze and predict complex relationships of nearly any kind of system. A very fast and effective learning process for a personal computer That means you can solve problems on your desktop in a reasonable time which you may have never thought possible. Modeling short and noisy data samples That means, you can deal with a problem as is and don't have to construct artificial conditions for your modeling method to get it work. Output of an optimally complex model Generally you can be sure to get a model at the end of the automated modeling process which can be expected not to be overfitted. Overfitted models are not able to predict inherent relationships between variables. Output of an analytical model as a transparent explanation component That means, you can evaluate the analytical model to explain the obtained results immediately after modeling.