A general technique is proposed for embedding online clustering algo- rithms based on competitive learning in a reinforcement learning frame- work. The basic idea is that the clustering system can be viewed as a rein- forcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforce- ment guided competitive learning (RGCL) algorithm is proposed that constitutes a reinforcement-based adaptation of learning vector quanti- zation (LVQ) with enhanced clustering capabilities.
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