Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/81829
Full metadata record
Full metadata record
DC FieldValueLanguage
dc.contributorGrupoM. Redes y Middlewarees_ES
dc.contributor.authorMaciá Pérez, Francisco-
dc.contributor.authorBerna-Martinez, Jose Vicente-
dc.contributor.authorFernández Oliva, Alberto-
dc.contributor.authorAbreu Ortega, Miguel-
dc.contributor.otherUniversidad de Alicante. Departamento de Tecnología Informática y Computaciónes_ES
dc.date.accessioned2018-10-11T12:41:17Z-
dc.date.available2018-10-11T12:41:17Z-
dc.date.issued2018-10-08-
dc.identifier.citationComplexity. 2018. Volume 2018, Article ID 4867607, 14 pages. doi:10.1155/2018/4867607es_ES
dc.identifier.issn1076-2787 (Print)-
dc.identifier.issn1099-0526 (Online)-
dc.identifier.urihttp://hdl.handle.net/10045/81829-
dc.description.abstractIn a data mining process, outlier detection aims to use the high marginality of these elements to identify them by measuring their degree of deviation from representative patterns, thereby yielding relevant knowledge. Whereas rough sets (RS) theory has been applied to the field of knowledge discovery in databases (KDD) since its formulation in the 1980s; in recent years, outlier detection has been increasingly regarded as a KDD process with its own usefulness. The application of RS theory as a basis to characterise and detect outliers is a novel approach with great theoretical relevance and practical applicability. However, algorithms whose spatial and temporal complexity allows their application to realistic scenarios involving vast amounts of data and requiring very fast responses are difficult to develop. This study presents a theoretical framework based on a generalisation of RS theory, termed the variable precision rough sets model (VPRS), which allows the establishment of a stochastic approach to solving the problem of assessing whether a given element is an outlier within a specific universe of data. An algorithm derived from quasi-linearisation is developed based on this theoretical framework, thus enabling its application to large volumes of data. The experiments conducted demonstrate the feasibility of the proposed algorithm, whose usefulness is contextualised by comparison to different algorithms analysed in the literature.es_ES
dc.description.sponsorshipThis work has been supported by University of Alicante projects GRE14-02 and Smart University.es_ES
dc.languageenges_ES
dc.publisherHindawi Publishing Corporationes_ES
dc.rights© 2018 Francisco Maciá Pérez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.es_ES
dc.subjectRough sets theoryes_ES
dc.subjectVariable precision rough setses_ES
dc.subjectOutlier probabilityes_ES
dc.subject.otherArquitectura y Tecnología de Computadoreses_ES
dc.titleApplication of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Elementes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.peerreviewedsies_ES
dc.identifier.doi10.1155/2018/4867607-
dc.relation.publisherversionhttps://doi.org/10.1155/2018/4867607es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
Appears in Collections:INV - GrupoM - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
Thumbnail2018_Macia_etal_Complexity.pdf1,93 MBAdobe PDFOpen Preview


This item is licensed under a Creative Commons License Creative Commons