Unsupervised recursive sequence processing

The self-organizing map (SOM) is a valuable tool for data visualization and data mining for potentially high-dimensional data of an a priori fixed dimensionality. We investigate SOMs for sequences and propose the SOM-S architecture for sequential data. Sequences of potentially infinite length are recursively processed by integrating the currently presented item and the recent map activation, as proposed in the SOMSD presented in (IEEE Trans. Neural Networks 14(3) (2003) 491). We combine that approach with the hyperbolic neighborhood of Ritter (Proceedings of PKDD-01, Springer, Berlin, 2001, pp. 338-349), in order to account for the representation of possibly exponentially increasing sequence diversification over time. Discrete and real-valued sequences can be processed efficiently with this method, as we will show in experiments. Temporal dependencies can be reliably extracted from a trained SOM. U-matrix methods, adapted to sequence processing SOMs, allow the detection of clusters also for real-valued sequence elements.