Extensions of Vector Quantization for Incremental Clustering

In this paper, we extend the conventional vector quantization by incorporating a vigilance parameter, which steers the tradeoff between plasticity and stability during incremental on-line learning. This is motivated in the adaptive resonance theory network (ART) approach and is exploited in our paper for forming a one-pass incremental and evolving variant of vector quantization. This variant can be applied for online clustering, classification and approximation tasks with an unknown number of clusters. Additionally, two novel extensions are described: one concerns the incorporation of the sphere of influence of clusters in the vector quantization learning process by selecting the 'winning cluster' based on the distances of a data point to the surface of all clusters. Another one introduces a deletion of cluster satellites and an online split-and-merge strategy: clusters are dynamically split and merged after each incremental learning step. Both strategies prevent the algorithm to generate a wrong cluster partition due to a bad a-priori setting of the most essential parameter(s). The extensions will be applied to clustering of two- and high-dimensional data, within an image classification framework and for model-based fault detection based on data-driven evolving fuzzy models.