Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Two topographic maps for data visualisation
Data Mining and Knowledge Discovery
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A family of novel clustering algorithms
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Clustering with alternative similarity functions
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
A novel construction of connectivity graphs for clustering and visualization
WSEAS Transactions on Computers
Immediate Reward Reinforcement Learning for Clustering and Topology Preserving Mappings
Similarity-Based Clustering
Hi-index | 0.00 |
We show how a previously derived method of using reinforcement learning for supervised clustering of a data set can lead to a sub-optimal solution if the cluster prototypes are initialised to poor positions. We then develop three novel reward functions which show great promise in overcoming poor initialization. We illustrate the results on several data sets. We then use the clustering methods with an underlying latent space which enables us to create topology preserving mappings. We illustrate this method on both real and artificial data sets.