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A new outlier detection method for high dimensional fuzzy databases based on LOF | ||
| Journal of Mathematical Modeling | ||
| مقاله 1، دوره 6، شماره 2، اسفند 2018، صفحه 123-136 اصل مقاله (543.58 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2018.8102.1108 | ||
| نویسندگان | ||
| Alireza Fakharzadeh Jahromi* 1؛ Zahra Ebrahimi Mimand2 | ||
| 1Department of Applied Mathematics, Shiraz University of Technology, Shiraz, Iran & Fars Elites Foundation, Shiraz, Iran, P.O. Box 71966-98893 | ||
| 2PayamNoor University, Shiraz Branch, shiraz, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Fuzzy numbers؛ Outlier data؛ LOF factor؛ $alpha$-cut؛ Left and right scoring | ||
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