Open AccessA Review on Outliers-Detection Methods for Multivariate Data
Author(s) -
Sharifah Sakinah Syed Abd Mutalib,
Siti Zanariah Satari,
Wan Nur Syahidah Wan Yusoff
Publication year - 2021
Language(s) - English
Resource type - Journals
ISSN - 2180-3102
DOI - 10.22452/josma.vol3no1.1
Subject(s) - multivariate statistics , outlier , projection pursuit , multivariate analysis , anomaly detection , computer science , data mining , strengths and weaknesses , dimension (graph theory) , artificial intelligence , statistics , pattern recognition (psychology) , mathematics , machine learning , psychology , social psychology , pure mathematics
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.