Insights: Software that extracts knowledge from data. Model. Predict. Simulate. Discover. Associate. 
			Gain new Knowledge and Insights from noisy data. Easily and Reliably.
    		Ultra-fast, parallel, self-organizing, high-dimensional modeling of real-world processes and complex systems.
Insights: Self-organizing Modeling. The next generation of knowledge mining.
This year we are celebrating the 100th Anniversary of A.G. Ivakhnenko, 
    the author of the unparalleled self-organizing, noise immune, inductive modeling and knowledge mining technology known as GMDH. He originated essential ideas found in many other 
    data mining methods today, and he published over 40 books and more than 500 papers, which truely made him an outstanding scientist in his field. A major difficulty in modeling complex systems in such 
    unstructured areas as economics, ecology, sociology, and others is the problem of the researcher introducing his or her own prejudices into the model. Since the system in question may be extremely complex, t
    he basic assumptions of the modeler may be vague guesses at best. It is not surprising that many of the results in these areas are vague, ambiguous, and extremely qualitative in nature.
Don't care about dimensionality of your data sets any longer. High-dimensional modeling in Insights 
					scales not only to data sets of very small  to large number of samples 
					but also from small to very large number of potential input variables. You simply self-organize models from 
					data directly.
					This applies even for so-called under-determined modeling tasks, i.e., 
					when the number of data samples is smaller than the number of input variables. Insights ensures that the appropriate modeling 
					and noise filtering algorithm is used to get reliable and compact predictive model composits.
There are many cases in practice where it is impossible to create analytical models using classical theoretical 
					systems analysis since there is incomplete knowledge of the processes involved. Environmental, medical and socio-economic 
					systems are but three examples. KnowledgeMiner Software is supporting the FuturICT project, a 
					major international public effort on modeling, understanding, and simulating our complex, global socio-eco-economic world.
					There are a lot of complex problems, which do need decision-making, but the means - the models - for understanding, 
					predicting, simulating, and where possible controlling such systems are simply missing increasingly. 
					A more powerful and easy-to-use tool that fills this knowledge gap is self-organizing modeling. Obtaining a model from data is easy. Obtaining a model from data that reliably reflects the underlying relationship
				  	in the data with some certainty is hard work. This is especially true for noisy, disturbed data. Noisy data are everywhere so you 
				  	most probably will use them. To systematically avoid overfitting - that is, when the model fits to random cases (noise) and therefore 
				  	can only have poor predictive power, which makes it useless -, to get optimized transfer functions in Active Neurons, and to self-organize robust optimal
				  	complex models with optimal predictive power, Insights employs original concepts of model validation at different levels of
				  	the self-organizing modeling process. Together with our Live Prediction Validation, application of data mining models is now more reliable,
				  	stable, and valuable than ever before. Until today, in data mining, model validation ends where modeling finishes. The model is then
				  	applied in real processes for prediction, classification, recommendation, diagnosis as if it would
				  	work stable and correct under all circumstances. But by far, it actually does not. Data mining models
				  	are always built on a finite set of data with certain properties. Therefore, like any model, they reflect the
				  	underlying object or process incomplete and approximately, only. Their validity is limited in space and/or time which
				  	defines their actual applicability domain.
				  	If a model works in its applicability domain or not only depends on the
				  	input data given to the model at runtime to predict an outcome. If a model works outside its domain it gets
				  	instable with irregular and presumably false prediction. How can a prediction respectively model be used with good conscience if there is no indication
				  	if it is applicable at all to the given inputs? The situation that a model works outside its applicability domain is
				  	not exceptional but can happen very often, especially for nonlinear models which are common in data mining.
We at KnowledgeMiner Software have taken this problem serious and we are proud that we came up with a powerful, original solution that makes
				  	model application in Insights much more trustworthy.
				  	For every single prediction you calculate, you also get the information
				  	if and to which extent the model is working in its domain. This makes further use of the prediction in your decision process safer
				  	and more robust.
A.G. Ivakhnenko, is the author of the unparalleled self-organizing, noise immune, inductive modeling and knowledge mining technology known as GMDH. A major difficulty in modeling complex systems in such unstructured areas as economics, ecology, sociology, and others is the problem of the researcher introducing his or her own prejudices into the model. Since the system in question may be extremely complex, the basic assumptions of the modeler may be vague guesses at best. It is not surprising that many of the results in these areas are vague, ambiguous, and extremely qualitative in nature.
Obtaining a model from data is easy. Obtaining a model from data that reliably reflects the underlying relationship in the data with some certainty is hard work. This is especially true for noisy data. Noisy data are everywhere so you most probably will use them. To systematically avoid overfitting - that is, when the model fits to random cases (noise) and therefore can only have poor predictive power, which makes it useless -, to get optimized transfer functions in Active Neurons, and to self-organize robust optimal complex models with optimal predictive power, Insights employs original concepts of model validation at different levels of the self-organizing modeling process. Together with our Live Prediction Validation, application of data mining models is now more reliable, stable, and valuable than ever before.
Import your data from Excel right away into Insights by a 
    		single mouse click. This is the most convenient and fast way to start modeling from your data. But also the reverse way works: 
    		Export your developed models and selected input data to Excel
			and further use them in your favorite way. No matter if it is a 
			single model or a model composite you also can have Insights 
			generating related plots for you. Employ your models in Excel with no additional effort.
Insights is the new brand of our former KnowledgeMiner product.
© 2001-2014 KnowledgeMiner Software
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