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|Type:||Artigo de evento|
|Title:||A New Tree-structured Self-organizing Map For Data Analysis|
De Andrade Netto M.L.
|Abstract:||Self-organizing map has been applied to a variety of tasks including data visualization and clustering. Once the point density of the neurons approximates the density of data, it is possible to miner clustering information from the data set after its unsupervised learning by using the neuron's relations. This paper presents a new algorithm for dynamical generation of a hierarchical structure of self-organizing maps with applications to data analysis. Differently from other tree-structured SOM approaches, which nodes are neurons, in this case the tree nodes are actually maps. From top to down, maps are automatically segmented by using the U-matrix information, which presents relations between neighboring neurons. The automatic map partitioning algorithm is based on mathematical morphology segmentation and it is applied to each map in each level of the hierarchy. Clusters of neurons are automatically identified and labeled and generate new sub-maps. Data are partitioned accordingly the label of its best match unit in each level of the tree. The algorithm may be seen as a recursive partition clustering method with multiple prototypes cluster representation, which enables the discoveries of clusters in a variety of geometrical shapes.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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