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|Type:||Artigo de evento|
|Title:||Cluster Analysis Using Self-organizing Maps And Image Processing Techniques|
|Author:||Costa Jose Alfredo F.|
de Andrade Netto Marcio L.
|Abstract:||Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into groups based on their similarities. Often the real number of categories may not be known a priori, and methods such as k-means may impose a structure on data rather than finding it. The self-organizing feature map (SOM) has been widely studied as a software tool for visualization of high-dimensional data. Some advantages in using SOM include information compression and density estimation while trying to preserve topological and metric relationship of the primary data items. This paper focus the usage of SOM as a clustering tool and some of the additional procedures required to enable a meaningful cluster's interpretation in the trained map. Topics discussed here include the usage of mathematical morphology segmentation method watershed to segment the neuron's distance image (u-matrix). Finding good watershed markers and the modification of the u-matrix homotopy are discussed. The algorithm automatically produces labeled sets of neurons that are related to the clusters in the P-dimensional space. An example of non-spherical, complex shaped and non-linearly separable clusters illustrate the capabilities of the method.|
|Editor:||IEEE, United States|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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