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Fecha
2021-09-02
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Institute of Electrical and Electronics Engineers (IEEE)
Citas
Resumen
The design and analysis of experimental research in Data Mining (DM) is anchored in a correct choice of the type of task addressed (clustering, classification, regression, etc.). However, although DM is a relatively mature discipline, there is no consensus yet about what is the taxonomy of DM tasks, which are their formal characteristics, and their corresponding metrics. In this paper, we formalize DM tasks in terms of Measurement Theory, which is a cornerstone of quantitative research in many disciplines, but has not yet been incorporated (in a consensual way) into some areas of Computer Science, including DM. The proposed formal framework provides a methodology to precisely define DM tasks for any given scenario and identify appropriate metrics. We validate this framework via (i) its coverage of existing DM tasks, (ii) its capability to group existing metrics into families, and (iii) its coverage of actual DM research problems, using about 250 papers from ACM KDD 2019 and IEEE ICDM 2019 conferences as reference sample.
Descripción
This is the accepted manuscript of the article. The registered version was first published in IEEE Transactions on Knowledge and Data Engineering, 35(2), 2147-2157, is available online at the publisher's website: https://doi.org/10.1109/TKDE.2021.3109823
Este es el manuscrito aceptado del artículo. La versión registrada fue publicada por primera vez en IEEE Transactions on Knowledge and Data Engineering, 35(2), 2147-2157, está disponible en línea en el sitio web del editor: https://doi.org/10.1109/TKDE.2021.3109823
Este es el manuscrito aceptado del artículo. La versión registrada fue publicada por primera vez en IEEE Transactions on Knowledge and Data Engineering, 35(2), 2147-2157, está disponible en línea en el sitio web del editor: https://doi.org/10.1109/TKDE.2021.3109823
Categorías UNESCO
Palabras clave
Data Mining, Tasks, Metrics, Measurement Theory
Citación
Amigo, E., Gonzalo, J., & Mizzaro, S. (2023). What is My Problem? Identifying Formal Tasks and Metrics in Data Mining on the Basis of Measurement Theory. IEEE Transactions on Knowledge and Data Engineering, 35(2), 2147-2157. https://doi.org/10.1109/TKDE.2021.3109823
Centro
E.T.S. de Ingeniería Informática
Departamento
Lenguajes y Sistemas Informáticos