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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Visualization of Complex Datasets with the Self-Organizing Spanning Tree
verfasst von : Ezequiel López-Rubio, Esteban José Palomo, Rafael Marcos Luque Baena, Enrique Domínguez
Erschienen in: Advances in Computational Intelligence
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Visualization of real world data is a difficult task due to the high-dimensional and the complex structure in real datasets. Scientific data visualization requires a variety of mathematical techniques to transform high-dimensional data sets into simple graphical objects that provide a clearer understanding. In this work a Self-Organizing Spanning Tree is proposed, which is able to learn a tree topology without any prespecified structure. Experimental results are provided to show the good performance with synthetic and real data. Moreover, the proposed self-organizing model is applied to color vector quantization, whose comparative results are provided.