A variable-parameter unsupervised learning clustering neural network approach with application to machine-part group formation

Abstract

In this paper, we develop a new unsupervised learning clustering neural network method for clustering problems in general and for solving machine-part group formation problems in particular. We show that our new approach solves a very challenging problem in the area of machine-part group formation. A review of machine-part group formation methods and unsupervised learning artificial neural network methods is given. We modify the well-known competitive learning algorithm by using the generalized Euclidean distance, and a momentum term in the weight vector updating equations. The cluster structure can be adjusted by changing the coefficients in the generalized Euclidean distance. The algorithm is flexible and applicable to many practical problems. We also develop a neural network clustering system which can be used to cluster a 0-1 matrix into diagonal blocks. The developed neural network clustering system is independent of the initial matrix and gives clear final clustering results which specify the machin...