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Text clustering has been recognized as an important component in data mining. Self-Organizing Map (SOM) based models have been found to have certain advantages for clustering sizeable text data. However, current existing approaches lack in providing an adaptive hierarchical structure within in a single model. This paper presents a new method of hierarchical text clustering based on combination of latent semantic analysis (LSA) and hierarchical GSOM, which is called LSA-HGSOM method. The textdoi:10.5539/mas.v3n9p72 fatcat:2fmbxxmklzczzawppid6fg2krm