General fuzzy min-max neural network for clustering and classification

This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classif ication algorithms developed by Simpson, The GFMM method combines the super vised and unsupervised learning within a single training algorithm. The fus ion of clustering and classification resulted in an algorithm that can he u sed as pure clustering, pure classification, or hybrid clustering classific ation. This hybrid system exhibits an interesting property of finding decis ion boundaries between classes while clustering patterns that cannot be sai d to belong to any of existing classes. Similarly to the original algorithm s, the hyperbox fuzzy sets are used as a representation of clusters and cla sses. Learning is usually completed in a few passes through the data and co nsists of placing and adjusting the hyperboxes in the pattern space which i s referred to as an expansion-contraction process, The classification resul ts can be crisp or fuzzy. New data can be included without the need for ret raining. While retaining all the interesting features of the original algor ithms, a number of modifications to their definition have been made in orde r to accommodate fuzzy input patterns in the form of lower and upper bounds , combine the supervised and unsupervised learning, and improve the effecti veness of operations. A detailed account of the GFMM neural network, its comparison with the Simp son's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems a re given.