Extracted information quality, a comparative study in high and low dimensions
by Leandro Ariza-Jiménez; Luisa F. Villa; Nicolás Pinel; O. Lucia Quintero
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 19, No. 2, 2021

Abstract: 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.

Online publication date: Tue, 17-Aug-2021

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