@misc{10481/91493,
year = {2024},
month = {1},
url = {https://hdl.handle.net/10481/91493},
abstract = {Spherical embedding is an important tool in several fields of data analysis, including
environmental data, spatial statistics, text mining, gene expression analysis, medical
research and, in general, areas in which the geodesic distance is a relevant factor. Many
data acquisition technologies are related to massive data acquisition, and these highdimensional
vectors are often normalised and transformed into spherical data. In this
representation of data on spherical surfaces, multidimensional scaling plays an important
role. Traditionally, the methods of clustering and representation have been combined,
since the precision of the representation tends to decrease when a large number of objects
are involved, which makes interpretation difficult. In this paper, we present a model that
partitions objects into classes while simultaneously representing the cluster centres on a
spherical surface based on geodesic distances. The model combines a partition algorithm
based on the approximation of dissimilarities to geodesic distances with a representation
procedure for geodesic distances. In this process, the dissimilarities are transformed in
order to optimise the radius of the sphere. The efficiency of the procedure described
is analysed by means of an extensive Monte Carlo experiment, and its usefulness is
illustrated for real data sets.},
organization = {Grant RTI2018-099723-B-I00, Ministry of Science, Innovation and
Universities of Spain, co-financed by FEDER},
organization = {Grant B-CTS-184-UGR20 funded by ERDF, EU/Ministry of Economic Transformation, Industry, Knowledge and Universities of Andalusia},
organization = {Austral University of
Chile},
organization = {The Pacific Alliance Grant of the Carolina Foundation},
publisher = {Springer Nature},
keywords = {Multidimensional scaling},
keywords = {Dissimilarity},
keywords = {Geodesic distance},
title = {Clustering and Geodesic Scaling of Dissimilarities on the Spherical Surface},
doi = {10.1007/s13253-023-00597-4},
author = {Vera Vera, José Fernando and Subiabre, Ricardo and Macías, Rodrigo},
}