Abstract: A Tree-structured Growing Self-Organizing Maps (TGSOM) is presented as an extended version of the Self-Organizing Feature Maps (SOFM), which has significant advantages for data mining applications. The TGSOM algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the TGSOM, 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 TGSOM. Such hierarchical clustering allows the data analyst to identify significant and interesting clusters at a higher level of the hierarchy, and as such continue with finer clustering of only the interesting clusters.