Clustering with Reinforcement Learning

Lecture notes in computer sciencePublished 6 December 2007

Wesam Ashour Barbakh, Colin Fyfe

DOI10.1007/978-3-540-77226-2_52

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Citations18

SJR quartileQ2

SJR score0.35

SNIP0.55

TL;DR

Three novel reward functions are developed which show great promise in overcoming poor initialization of cluster prototypes and are used to create topology preserving mappings in clustering methods.

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.

Keywords

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