A clustering algorithm based on graph connectivity
Information Processing Letters
Graph Characteristic from the Gauss-Bonnet Theorem
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Metric Methods in Surface Triangulation
Proceedings of the 13th IMA International Conference on Mathematics of Surfaces XIII
Isometric Embeddings in Imaging and Vision: Facts and Fiction
Journal of Mathematical Imaging and Vision
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Clustering is a technique extensively employed for the analysis, classification and annotation of DNA microarrays. In particular clustering based upon the classical combinatorial curvature is widely applied. We introduce a new clustering method for vertex-weighted networks, method which is based upon a generalization of the combinatorial curvature. The new measure is of a geometric nature and represents the metric curvature of the network, perceived as a finite metric space. The metric in question is natural one, being induced by the weights. We apply our method to publicly available yeast and human lymphoma data. We believe this method provides a much more delicate, graduate method of clustering then the other methods which do not undertake to ascertain all the relevant data. We compare our results with other works. Our implementation is based upon Trixy (as available at http://tagc.univ-mrs.fr/bioinformatics/trixy.html), with some appropriate modifications to befit the new method.