A PRUNING APPROACH TO PATTERN DISCOVERY

In this study, we proposed a general pruning procedure to reduce the dimension of a large database so that the properties of the extracted subset can be well defined. Since learning functions have been widely applied, we take this group of functions as an example to demonstrate the proposed procedure. Based on the concept of Support Vector Machine (SVM), three major stages of preliminary pruning, fitting function, and refining are proposed to discover a subset that possess the characteristics of some learning function from the given large data set. Three models were used to illustrate and evaluate the proposed pruning procedure and the results have shown to be promising in application.