ACM Computing Surveys (CSUR)
Modelling competitive Hopfield networks for the maximum clique problem
Computers and Operations Research
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural techniques for combinatorial optimization with applications
IEEE Transactions on Neural Networks
Design and analysis of maximum Hopfield networks
IEEE Transactions on Neural Networks
Multi-start Stochastic Competitive Hopfield Neural Network for p-Median Problem
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Expert Systems with Applications: An International Journal
Improved watershed transform for tumor segmentation: Application to mammogram image compression
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
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.