OUTLIER DETECTION ON HIGH DIMENSIONAL DATA USING MINIMUM VECTOR VARIANCE (MVV)

High-dimensional data can occur in actual cases where the variable p is larger than number of observations n. The problem that often occurs when adding dimensions indicates points will approach an outlier. Outliers are part do not follow distribution pattern and located far from center. existence outliers needs to be detected because it lead deviations analysis results. One methods used detect Mahalanobis distance. To obtain a robust distance, Minimum Vector Variance (MVV) method used. This study compare MVV with classical distance detecting non-invasive blood glucose level data, both at p>n n>p. test results show better for shows more effective identifying minimum group outlier method.