Adapting to Increasing Data Availability Using Multi-layered Self-Organising Maps

Often in clustering scenarios, the data analyst does not have access to a complete data set at the outset and new data dimensions might only become available at some later time. In this case it is useful to be able to cluster the available data and have some mechanism for incorporating new dimensions as they become available without having to recluster all the data from scratch (which may not be feasible for on-line learning scenarios). This paper utilises an established mechanism for interconnecting multiple Self-Organising Maps to achieve this aim and reveals a useful way of visualising the affect of individual dimensions on the structure of clusters.