Models of incremental concept formation
Artificial Intelligence
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Self-organising modelling as a part of simulation process
Systems Analysis Modelling Simulation - Special issue: Self-organising modelling and simulation
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Three self-organizing data mining technologies that employ complementary descriptive languages - parametric regression models (GMDH neural networks), fuzzy rules (self-organizing fuzzy rule induction), and similarity models (analog complexing based clustering and classification) - are applied to generate diagnosis models of different levels of heart disease. The classification results show an accuracy of over 95% in average. Due to the strong knowledge extraction capabilities of the used technologies a nucleus of 4 most relevant variables is identified. The obtained results both classification accuracy and identified nucleus are also important for diagnosis cost reduction considerations.