Di Matteo, TizianaAste, TomasoHyde, StephenRamsden, Stuart2015-12-130378-4371http://hdl.handle.net/1885/79619We propose a general method to study the hierarchical organization of financial data by embedding the structure of their correlations in metric graphs in multi-dimensional spaces. An application to two different sets of interest rates is discussed by constructing triangular embeddings on the sphere. Three-dimensional representations of these embeddings with the correct metric geometry are constructed and visualized. The resulting graphs contain the minimum spanning tree as a sub-graph and they preserve its hierarchical structure. This produces a clear cluster differentiation and allows us to compute new local and global topological quantities.Keywords: Conformal mapping; Cost effectiveness; Data acquisition; Economics; Elastic moduli; Finance; Marketing; Correlations; Data clustering; Econophysics; Interest rates; Large scale systems Correlations; Data clustering; Econophysics; Interest rates200510.1016/j.physa.2005.02.0632015-12-11