Window Merge Neural Gas for Processing Pattern Sequences

In this paper an extension of the merge neural gas (MNG) model for processing data sequences is presented. In the proposed Window MNG network, time windows are used for processing signal inputs and their contexts. The new strategy allows breaking complex patterns into sequences of simpler patterns. A recently proposed projection method is adapted for projecting the multidimensional vectors that result from the temporal vector quantization onto two-dimensional maps. In addition, a new training method for MNG networks is proposed that allows obtaining enhanced results. The proposed methods are applied to the visualization and clustering of the Mackey-Glass time series and control chart signals.