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| The Interrelation Miner Methodology is a new simple, powerful, and reliable technology for multivariate statistical data analysis and outcome prediction (data mining) of either complex and/or multidimensional data sets or of very small datasets based on statistical fuzzy set theory. Not only can this tool be applied to any complicated data set with very many samples or very many variables (e.g. micro array data), it also provides best results for small or incomplete data tables, because it uses very simple decision rules with nonlinear data models. Experience: The Interrelation Miner has very successfully analyzed GC/MS data and has provided up to 20% better prediction performance than compared approaches, such as neural networks and other complex multivariate predictions. Other possible applications include, among others: Chromatography, mass spectrography, microarrays, protein arrays, environmental data sets, food quality control, quality control in general, etc.Contents Long Description |
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