Dynamic self-organizing maps with controlled growth for knowledge discovery

The growing self-organizing map (GSOM) has been presented as an extended ve rsion of the self-organizing map (SOM), which has significant advantages fo r knowledge discovery applications. In this paper, the GSOM algorithm is pr esented in detail and the effect of a spread factor, which can be used to m easure and control the spread of the GSOM, is investigated. The spread fact or is independent of the dimensionality of the data and as such can be used as a controlling measure for generating maps with different dimensionality , which can then be compared and analyzed with better accuracy. The spread factor is also presented as a method of achieving hierarchical clustering o f a data set with the GSOM. Such hierarchical clustering allows the data an alyst to identify significant and interesting clusters at a higher level of the hierarchy, and as such continue with finer clustering of only the inte resting clusters. Therefore, only a small map is created in the beginning w ith a low spread factor, which can be generated for even a very large data set, Further analysis is conducted on selected sections of the data and as such of smaller volume, Therefore, this method facilitates the analysis of even very large data sets.