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Published in:
2007 | OriginalPaper | Chapter
Visualization of Topology Representing Networks
Authors : Agnes Vathy-Fogarassy, Agnes Werner-Stark, Balazs Gal, Janos Abonyi
Published in: Intelligent Data Engineering and Automated Learning - IDEAL 2007
Publisher: Springer Berlin Heidelberg
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As data analysis tasks often have to face the analysis of huge and complex data sets there is a need for new algorithms that combine vector quantization and mapping methods to visualize the hidden data structure in a low-dimensional vector space. In this paper a new class of algorithms is defined. Topology representing networks are applied to quantify and disclose the data structure and different nonlinear mapping algorithms for the low-dimensional visualization are applied for the mapping of the quantized data. To evaluate the main properties of the resulted topology representing network based mapping methods a detailed analysis based on the wine benchmark example is given.