|Title||Hierarchical Spherical Clustering|
|Publication Type||Journal Article|
|Year of Publication||2002|
|Authors||Torra V, Miyamoto S|
|Journal||Int. J. of Uncertainty Fuzziness and Knowledge-Based Systems|
This work introduces an alternative representation for large dimensional data sets. Instead of using 2D or 3D representations, data is located on the surface of a sphere. Together with this representation, a hierarchical clustering algorithm is defined to analyse and extract the structure of the data. The algorithm builds a hierarchical structure (a dendrogram) in such a way that different cuts of the structure lead to different partitions of the surface of the sphere. This can be seen as a set of concentric spheres, each one being of different granularity. Also, to obtain an initial assignment of the data on the surface of the sphere, a method based on Sammon's mapping has been developed.
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