Algorithm for the detection of outliers based on the theory of rough sets

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Title: Algorithm for the detection of outliers based on the theory of rough sets
Authors: Maciá Pérez, Francisco | Berna-Martinez, Jose Vicente | Fernández Oliva, Alberto | Abreu Ortega, Miguel
Research Group/s: GrupoM. Redes y Middleware
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Knowledge discovery | Detection of outliers | Rough set theory
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: Jul-2015
Publisher: Elsevier
Citation: Decision Support Systems. 2015, 75: 63-75. doi:10.1016/j.dss.2015.05.002
Abstract: Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.
Sponsor: This work was performed as part of the Smart University Project (SmartUniversity2014) financed by the University of Alicante.
ISSN: 0167-9236 (Print) | 1873-5797 (Online)
DOI: 10.1016/j.dss.2015.05.002
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2015 Elsevier B.V.
Peer Review: si
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Appears in Collections:INV - GrupoM - Artículos de Revistas

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