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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Vathy-Fogarassy, A., Werner-Stark, A., Gal, B., & Abonyi, J. (2007). Visualization of topology representing networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4881 LNCS, pp. 557–566). Springer Verlag. https://doi.org/10.1007/978-3-540-77226-2_57
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