A review on outliers-detection methods for multivariate data

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Abstract

Data in practice are often of high dimension and multivariate in nature. Detection of outliers has been one of the problems in multivariate analysis. Detecting outliers in multivariate data is difficult and it is not sufficient by using only graphical inspection. In this paper, a nontechnical and brief outlier detection method for multivariate data which are projection pursuit method, methods based on robust distance and cluster analysis are reviewed. The strengths and weaknesses of each method are briefly discussed.

Item Type:	Article
Subjects:	Q Science > QA Mathematics
Faculty/Division:	Center for Mathematical Science
Depositing User:	Ms. Siti Zanariah Satari
Date Deposited:	19 Jul 2021 07:23
Last Modified:	19 Jul 2021 07:23
URI:	http://umpir.ump.edu.my/id/eprint/31672
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