Extracted information quality, a comparative study in high and low dimensions

Leandro Ariza-Jimenez, Luisa F. Villa, Nicolas Pinel, O. Lucia Quintero

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

1 Cita (Scopus)


Uncovering interesting groups in either multidimensional or network spaces has become an essential mechanism for data exploration and understanding. Decision making requires relevant information as well as high-quality on the retrieved conclusions. We presented a comparative study of two compact representations drawn from the same set of data objects by clustering high-dimensional spaces and low-dimensional Barnes-Hut t-stochastic neighbour embeddings. There is no consensus on how the problem should be addressed and how these representations/models should be analysed because of their different notions. We introduced a measure to compare their results and capability to provide insights into the information retrieved. We considered low-dimensional embeddings as a potentially revealing strategy to uncover dynamics possibly not uncovered in big-data spaces. We demonstrated that a non-guided approach can be as revealing as a user-guided approach for data exploration and presented coherent results for good uncertainty modelling capability in terms of fuzziness and densities.

Idioma originalInglés
Páginas (desde-hasta)214-241
Número de páginas28
PublicaciónInternational Journal of Business Intelligence and Data Mining
EstadoPublicada - 2021


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