KnowledgeMiner X 5.3 review
DownloadKnowledgeMiner is a powerful, easy-to-use modeling and prediction tool designed to support the process of knowledge extraction from data on a highly automated level.
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KnowledgeMiner is a powerful, easy-to-use modeling and prediction tool designed to support the process of knowledge extraction from data on a highly automated level.
KnowledgeMiner X is an artificial intelligence tool designed to easily extract hidden knowledge from data.
It works on two advanced self-organizing modeling technologies: Group Method of Data Handling(GMDH), and Analog Complexing.
Built onthe cybernetic principles of self organization, KnowledgeMiner brings high-end modeling capabilities to your desktop.
KnowledgeMiner can help you answer questions like:
Environment: "Is the Earth getting warmer?"
Economy: "When will the U.S. recover from the recession?"
Finance: "What will be the closing price of my favorite stock?"
Markets: "How much should we charge for our new product?"
It's also excellent for: Ecology, Biotechnology, Chemistry, Math, Sociology, Engineering, Medicine, and much more.
Here are some key features of "KnowledgeMiner X":
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.
KnowledgeMiner 5.0 works on three advanced inductive learning modeling algorithms.
Limitations:
This demo is limited in several ways, including the inability to save, print, or accept more than 25 input variables.
What's New:
[new] automated fuzzy model based defuzzification implemented, which simplifies use of fuzzy models very much;
[new] defuzzification model self-detects if related fuzzy models are available and takes their outputs for back-transforming results into the initial data space;
[new] works on both static and dynamic fuzzy models;
[fixed] bug that identifies defuzzification models as dedicated classification models;
[fixed] bug with decimal point/comma conversion issues in the regression model export routine.
KnowledgeMiner X 5.3 keywords