Abstract
The Big Data analysis allows to generate knowledge based on mathematical models that surpass human capabilities, and therefore it is necessary to have robust computer systems. In this connection, the dimensionality reduction (DR) allows to perform approximations to make data perceptible in a simple and compact way while also the computational cost is reduced. Additionally, interactive interfaces enable the user to work with algorithms involving complex mathematical and statistical processes typically aimed at providing weighting factors to each RD algorithm to find the best way to represent data at a low dimension. In this study, a bibliographic re-view of the different models of interactive interfaces for the analysis of Big Data using RD is presented, by considering different, existing proposals and approaches on how to display the information. Particularly, those approaches based on mental processes and uses of color along with an intuitive handling are of special interest.
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The authors thank the SDAS Research Group (http://sdas-group.com) and “Universidad Técnica del Norte”.
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Umaquinga-Criollo, A.C., Peluffo-Ordóñez, D.H., Rosero-Montalvo, P.D., Godoy-Trujillo, P.E., Benítez-Pereira, H. (2020). Interactive Visualization Interfaces for Big Data Analysis Using Combination of Dimensionality Reduction Methods: A Brief Review. In: Basantes-Andrade, A., Naranjo-Toro, M., Zambrano Vizuete, M., Botto-Tobar, M. (eds) Technology, Sustainability and Educational Innovation (TSIE). TSIE 2019. Advances in Intelligent Systems and Computing, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-030-37221-7_17
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