부산대학교 도서관

This paper presents a data mining technique for qualitative analysis of Hodgkin-Huxley model of cell excitability. Such problem cannot be solved analytically. Therefore we apply Monte-Carlo techniques for the generation of model parameters, and use data mining algorithm for classification of learning tuples obtained. As a result we attain a decision tree capable of classifying the excitability depending on the relationship between initial conditions and between rate constants. Details of software development and issues of computational complexity are presented. The software includes the package of Java-classes. An example for classification of excitability of three types is presented