Data Mining using Modified GFMM Neural Network

Abstract

The fuzzy neural networks are adaptive, learns quickly and are highly suitable in decision making where uncertainty is involved. In this paper the Modified General Fuzzy Min-Max Neural Network (MGFMMNN) is described which is experimented for the data mining tasks such as classification and clustering. The MGFMMNN utilizes fuzzy sets as pattern classes in which each fuzzy set is a union of fuzzy set hyperboxes. It is an extension of the general fuzzy min-max (GFMM) neural network proposed by Gabrys and Bargiala. The data mining tasks such as classification and clustering have been studied using MGFMMNN and Fisher Iris data set. Further, MGFMMNN is trained using Hepatitis Data Set to verify its classification and recognition ability. The results obtained are awfully persuading and confirms the effectiveness of the proposed system. The technique proposed is quick and reliably deployable in the applications that need classification and clustering. General Terms Data Mining, Fuzzy Systems, Neural Networks, Pattern Recognition.