Abstract
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.
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Barbakh, W., Fyfe, C. (2007). Clustering with Reinforcement Learning. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_52
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DOI: https://doi.org/10.1007/978-3-540-77226-2_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
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