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- Title
Method Non-Deterministic and Computationally Viable for Detecting Outliers in Large Datasets.
- Authors
FERNÁNDEZ OLIVA, ALBERTO; MACIÁ PÉREZ, FRANCISCO; BERNÁ MARTINEZ, JOSÉ VICENTE; ABREU ORTEGA, MIGUEL ALFONSO
- 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.
- Subjects
ROUGH sets; OUTLIER detection; OUTLIERS (Statistics); GENERALIZATION; DATA mining
- Publication
Journal of Information Science & Engineering, 2020, Vol 36, Issue 3, p671
- ISSN
1016-2364
- Publication type
Article
- DOI
10.6688/JISE.202005_36(3).0012