Open AccessOutlier Detection Using Minimum Vector Variance Algorithm with Depth Function and Mahalanobis Distance
Author(s) -
Puji Puspa Sari,
Erna Tri Herdiani,
Nurtiti Sunusi
Publication year - 2021
Publication title -
jurnal matematika, statistika dan komputasi/jurnal matematika statistik dan komputasi
Language(s) - English
Resource type - Journals
eISSN - 2614-8811
pISSN - 1858-1382
DOI - 10.20956/j.v17i3.12629
Subject(s) - mahalanobis distance , outlier , sorting , anomaly detection , pattern recognition (psychology) , mathematics , computer science , algorithm , data mining , statistics , artificial intelligence
Outliers are observations where the point of observation deviates from the data pattern. The existence of outliers in the data can cause irregularities in the results of data analysis. One solution to this problem is to detect outliers using a statistical approach. The statistical approach method used in this study is the Minimum Vector Variance (MVV) algorithm which has robust characteristics for outliers. The purpose of this research is to detect outliers using the MVV algorithm by changing the data sorting criteria using the Robust Depth Mahalanobis to produce maximum detection. The results obtained from this study are that RDMMVV is superior to the observed value in showing the outliers and the location of the outliers in the data plot compared to DMMVV and MMVV.