Dynamic self-organizing maps with controlled growth for knowledge discovery.
Journal - IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council (United States )
The growing self-organizing map (GSOM) algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the GSOM, is investigated. The spread factor 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 of a data set with the GSOM. Such hierarchical clustering allows the data analyst to identify significant and interesting clusters at a higher level of the hierarchy, and continue with finer clustering of the interesting clusters only. Therefore, only a small map is created in the beginning with 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 of smaller volume. Therefore, this method facilitates the analysis of even very large data sets.