Sift Tutorial: Outlier Detection with PCA
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This tutorial will show you how to use the outlier detection methods built into Sift, when each method might be appropriate and how to interpret the results. Outlier detection is a key artifact of data analysis, identifying errant data or anomalies that might make further data analysis less effective. Within Sift, there are multiple methods for detecting outliers, but we will be focusing on doing so with PCA data in this tutorial.
Data
For this tutorial, we will be examining some data from a Visual3D Workshop at a recent ASB meeting.
(HipAngleX - Traces: 2 distinct groups and several outliers) (also HipAngleZ from auto builder)