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Anais do 7. Congresso Brasileiro de Redes Neurais
The self-organizing map (SOM) forms a nonlinear projection from a high-dimensional data onto a regular (usually) two-dimensional grid and have been widely studied as a software tool for visualization of high-dimensional data. However, the automation of knowledge discovery in SOM is not straightforward. A complementary stage is need in trained SOMs when dealing with complex data clustering problems. This paper investigates the usage of contiguity constrained hierarchical clustering in SOMdoi:10.21528/cbrn2005-213 fatcat:uoybsygunzf5lhcohe6u5ynsx4