By Ágnes Vathy-Fogarassy
This paintings offers an information visualization method that mixes graph-based topology illustration and dimensionality aid how to visualize the intrinsic information constitution in a low-dimensional vector area. the appliance of graphs in clustering and visualization has numerous merits. A graph of significant edges (where edges represent kin and weights characterize similarities or distances) offers a compact illustration of the full complicated information set. this article describes clustering and visualization tools which are in a position to make the most of info hidden in those graphs, in keeping with the synergistic mixture of clustering, graph-theory, neural networks, facts visualization, dimensionality relief, fuzzy equipment, and topology studying. The paintings includes a number of examples to assist within the knowing and implementation of the proposed algorithms, supported via a MATLAB toolbox on hand at an linked web site.
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