Stochastic optimal competitive Hopfield network for partitional clustering

This paper presents a stochastic optimal competitive Hopfield network to solve NP-hard partitional clustering problem. Cluster analysis has played a central role in different fields and is often adopted as an approach for preliminary and descriptive data analysis and classification. The objective of the partitional clustering problem is to partition a data set into a specified number of clusters according to certain criteria, e.g. a square error function, and therefore can be treated as an optimization problem. The proposed stochastic optimal competitive Hopfield network introduces a hill-climbing dynamics which helps the network escape from local minima, and therefore can find better cluster partition. The performance is evaluated through several benchmark data sets. The simulation results show that the stochastic optimal competitive Hopfield network outperforms previous approaches, such as optimal competitive Hopfield model, k-means, genetic algorithm, particle swarm optimization, differential evolution and combinatorial particle swarm optimization for partitional clustering problem.