Determining the Number of Clusters using Neural Network and Max Stable Set Problem

One of the most difficult problems, in cluster analysis is the determination of the number of clusters in a data set. Solving this problem consists in detecting and finding the best number of clusters, which is an input parameter for the clustering problems. In this paper, we propose a new approach using the Maximum Stable Set Problem (MSSP) combined by Continuous Hopfield Network (CHN) to determine the number of clusters, which is a basic input parameter for K-Means method. By testing the theoretical results, the proposed approach was validated on a real application for the text mining. Some numerical examples and computational experiments assess the effectiveness of this approach as demonstrated in this paper.