Proposed and tested an algorithm of using principles of Cantor, von Koch sets for exploratory fractals clinical pharmacological data analysis. The algorithm is based on the grouping data, formation of categorical variabilities in the form of subgroups as iteration process as for receiving Cantor, von Koch sets. It boils down to: selection of informative numerical dependent variabilities; transformation these informative numerical dependent variabilities to new categorical variabilities; formation categorical variabilities in the form of subgroups as a result of an iterative process as for Cantor, von Koch sets; statistical analysis of the data; determination of the distribution of variabilities; transformations that may be normalize from non-normal data; ANOVA - analysis of variance parametric data or nonparametric equivalent of ANOVA - Kruskal-Wallis testing; formulation of the conclusion. Our algorithm of using Cantor, von Koch sets principles for Exploratory Fractals Data Analysis of clinical pharmacological data will help maximize insight, uncover underlying structure, extract important variables, develop models and determine optimal factor settings.
Kulishov, S.K. and Iakovenko, O.M.
"Fractals As Triggers For Exploratory Statistical Analysis of Clinical Pharmacological Data,"
International Journal of Pharmacology and Pharmaceutical Technology: Vol. 1
, Article 11.
Available at: https://www.interscience.in/ijppt/vol1/iss1/11