@conference{umaquinga2020,

title = {Interactive Visualization Interfaces for Big Data Analysis Using Combination of Dimensionality Reduction Methods: A Brief Review},

author = {Ana C. Umaquinga-Criollo and Diego H. Peluffo-Ordóñez and Paúl D. Rosero-Montalvo and Pamela E. Godoy-Trujillo and Henry Benítez-Pereira},

editor = {Springer Charm },

url = {https://link.springer.com/chapter/10.1007/978-3-030-37221-7_17

https://doi.org/10.1007/978-3-030-37221-7_17},

doi = {https://doi.org/10.1007/978-3-030-37221-7_17},

isbn = {978-3-030-37220-0},

year  = {2020},

date = {2020-01-03},

booktitle = {Technology, Sustainability and Educational Innovation (TSIE)},

pages = {193-203},

publisher = {Springer, Cham},

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.},

note = { Print ISBN 978-3-030-37220-0 Online ISBN 978-3-030-37221-7 },

keywords = {Big data, Business intelligence, data mining, Dimensionality reduction, Interactive interface},

pubstate = {published},

tppubtype = {conference}

}

@conference{10.1007/978-3-319-92537-0_64,

title = {A Novel Color-Based Data Visualization Approach Using a Circular Interaction Model and Dimensionality Reduction},

author = {Jose Alejandro Salazar-Castro and Paul D Rosero-Montalvo and Diego Fernando Peña-Unigarro and Ana Cristina Umaquinga-Criollo and Zenaida Castillo-Marrero and Edgardo Javier Revelo-Fuelagán and Diego Hernán Peluffo-Ordóñez and César Germán Castellanos-Domínguez},

editor = {Tingwen Huang and Jiancheng Lv and Changyin Sun and Alexander V Tuzikov},

url = {https://link.springer.com/chapter/10.1007%2F978-3-319-92537-0_64},

doi = {10.1007/978-3-319-92537-0_64},

isbn = {978-3-319-92537-0},

year  = {2018},

date = {2018-05-26},

booktitle = {Advances in Neural Networks - ISNN 2018},

volume = {10878},

pages = {557-567},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Dimensionality reduction (DR) methods are able to produce low-dimensional representations of an input data sets which may become intelligible for human perception. Nonetheless, most existing DR approaches lack the ability to naturally provide the users with the faculty of controlability and interactivity. In this connection, data visualization (DataVis) results in an ideal complement. This work presents an integration of DR and DataVis through a new approach for data visualization based on a mixture of DR resultant representations while using visualization principle. Particularly, the mixture is done through a weighted sum, whose weighting factors are defined by the user through a novel interface. The interface's concept relies on the combination of the color-based and geometrical perception in a circular framework so that the users may have a at hand several indicators (shape, color, surface size) to make a decision on a specific data representation. Besides, pairwise similarities are plotted as a non-weighted graph to include a graphic notion of the structure of input data. Therefore, the proposed visualization approach enables the user to interactively combine DR methods, while providing information about the structure of original data, making then the selection of a DR scheme more intuitive.},

note = {Print ISBN: 978-3-319-92536-3},

keywords = {Data visualization, Dimensionality reduction, Interactive interface, Pairwise similarity},

pubstate = {published},

tppubtype = {conference}

}