Descriptive Data Mining

Descriptive data mining techniques are aimed at describing the data or the phenomenon underlying the data, and are generally applied from the side of unsupervised learning. In this last paradigm, the data and properties of the sets to be analyzed are characterized by the absence of a label, class or output within them. Also indicate that there is a set of descriptive techniques that are capable of analyzing and describing the relationships of data under supervised learning.

Nowadays, the analysis and description of data within Data Science is a great challenge since we are facing the necessity of analyzing large volumes of data, which in some situations are volatile, changing, heterogeneous and may come from different sources.

Contact: Cristóbal J. Carmona del Jesus

Related Researchers:

Letra:

  Name Email Area Cat.
Alcalá Fernández, Jesús jalcala@decsai.YK@F@0IeH6ugr.es Data Science and Big Data Area PhD
Alcalá Fernández, Rafael alcala@decscFB..cwai.ugr.es Data Science and Big Data Area PhD
Cano de Amo, José Ramón jrcano@ujgoenBHDBuaen.es Data Science and Big Data Area PhD
Carmona del Jesus, Cristóbal J. ccarmona@ujaen.42u1eqzqF3es Data Science and Big Data Area PhD
Casillas Barranquero, Jorge casillas@decsai.oOsFWICZ55Haugr.es Data Science and Big Data Area PhD
García Martínez, Carlos cgarcia@uc5DgAW4o.es Data Science and Big Data Area PhD
González García, Pedro pglezoOf6bGI@ujaen.es Data Science and Big Data Area PhD
Jesus Díaz, María José del mjjesus@ujaen9Wka__HQJ.es Data Science and Big Data Area PhD
Luna Ariza, Jose María jmluna@hy06S.KIuco.es Data Science and Big Data Area PhD
Melero Rus, Francisco Javier fjmelero@ugeGbrhVHLA.lyr.es DaSCI Technology Applications Area, Data Science and Big Data Area, Computational Intelligence Area PhD
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