This work attempts to design a method for computerbased
mathematical model of multi-parametric datasets that
their features are fully-automatic structural and parametric
optimization of models. This method has different inputs and
one output as a subset of components for the base function F. In
addition, several functions in modeling the classified rules are
used including 12 polynomials and 6 hyperbolic functions. This
work combines the advantage of data mining algorithms and
Fuzzy All-Permutation Rule Base (FARB). In order to evaluate
the performance of the proposed method, two classifications
based on association rules case studies were used in an attempt to
design a dynamic mathematical method. The first case study
takes the virtual rules which consist of four rules, while in the
second case study actual rules are selected. According to the
experimental results, not only enhanced accuracy can be achieved
by the proposed method, but also it can be used for intelligent
data analysis for huge and small datasets.