A Method Non-Deterministic and Computationally Viable for Detecting Outliers in Large Datasets

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Title: A Method Non-Deterministic and Computationally Viable for Detecting Outliers in Large Datasets
Authors: Fernández Oliva, Alberto | Maciá Pérez, Francisco | Berna-Martinez, Jose Vicente | Abreu Ortega, Miguel
Research Group/s: GrupoM. Redes y Middleware
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Outliers | Rough sets (RS) | RS basic model (RSBM) | Variable precision rough set model (VPRSM) | Data set | Data mining
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: May-2020
Publisher: Institute of Information Science, Academia Sinica
Citation: Journal of Information Science and Engineering. 2020, 36(3): 671-685. doi:10.6688/JISE.202005_36(3).0012
Abstract: This paper presents an outlier detection method that is based on a Variable Precision Rough Set Model (VPRSM). This method generalizes the standard set inclusion relation, which is the foundation of the Rough Sets Basic Model (RSBM). The main contribution of this research is an improvement in the quality of detection because this generalization allows us to classify when there is some degree of uncertainty. From the proposed method, a computationally viable algorithm for large volumes of data is also introduced. The experiments performed in a real scenario and a comparison of the results with the RSBM-based method demonstrate the efficiency of both the method and the algorithm in diverse contexts that involve large volumes of data.
Sponsor: This work has been supported by grant TIN2016-78103-C2-2-R, and University of Alicante projects GRE14-02 and Smart University.
URI: http://hdl.handle.net/10045/106950
ISSN: 1016-2364
DOI: 10.6688/JISE.202005_36(3).0012
Language: eng
Type: info:eu-repo/semantics/article
Rights: © Institute of Information Science, Academia Sinica
Peer Review: si
Publisher version: https://doi.org/10.6688/JISE.202005_36(3).0012
Appears in Collections:INV - GrupoM - Artículos de Revistas

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