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Published in:
2007 | OriginalPaper | Chapter
Clustering with Reinforcement Learning
Authors : Wesam Barbakh, Colin Fyfe
Published in: Intelligent Data Engineering and Automated Learning - IDEAL 2007
Publisher: Springer Berlin Heidelberg
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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.