An Extended Neural Gas Model for Efficient Data Mining Tasks

摘要

This paper presents a numerical association rule extraction method that is based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice. The proposed method copes with the usual problems of the symbolic association rule extraction method that are computation time and rule selection.