Open AccessDetection of Outliers in Multivariate Data using Minimum Vector Variance Method
Author(s) -
Erna Tri Herdiani,
Puji Puspa Sari,
Nurtiti Sunusi
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1341/9/092004
Subject(s) - mahalanobis distance , outlier , statistics , variance (accounting) , estimator , multivariate statistics , multivariate normal distribution , mathematics , pattern recognition (psychology) , computer science , robust statistics , data mining , artificial intelligence , accounting , business
Outliers are observations that do not follow the distribution of data patterns and can cause deviations from data analysis, so a method for identifying outliers is needed One method in scanning detection is Minimum Vector Variance which is a robust estimator that uses the minimum Vector Variance (VV) criteria. In this study, the MVV method was used to detect outliers in criminality data in Indonesia in 2013 and data that had been entered out by 5% and 10%. The results showed that the MVV method was more effective than the Mahalanobis distance when detecting outliers in data that had been entered out by 5% and 10%.