A new outlier detection method for high dimensional fuzzy databases based on LOF

Document Type : Research Article

Authors

1 Department of Applied Mathematics, Shiraz University of Technology, Shiraz, Iran & Fars Elites Foundation, Shiraz, Iran, P.O. Box 71966-98893

2 PayamNoor University, Shiraz Branch, shiraz, Iran

Abstract

Despite the importance  of fuzzy data and existence of  many powerful methods for determining crisp outliers, there are few approaches for identifying outliers in fuzzy database. In this regard, the present article introduces a new method for discovering outliers among a set of multidimensional data. In order to provide a complete fuzzy strategy, first we extend the density-based local outlier factor method (LOF), which is successfully applied for  identifying multidimensional crisp outliers. Next, by using the left and right scoring defuzzyfied method, a fuzzy data outlier degree is determined. Finally, the efficiency of the method in outlier detection is shown by numerical examples.

Keywords

Journal of Mathematical Modeling
Volume 6, Issue 2
December 2018
Pages 123-136

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