A Reinforcement Learning Approach to On-line Clustering

A general technique is proposed for embedding on-line clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the RGCL (Reinforcement Guided Competitive Learning) algorithm is proposed that constitutes a reinforcement-based adaptation of LVQ with enhanced clustering capabilities. In addition, we suggest extensions of RGCL and LVQ that are characterized by the property of sustained exploration and sig-niicantly improve the performance of those algorithms as indicated by experimental tests on well-known datasets.