Sift Tutorial: Outlier Detection with PCA

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. We first need to load the .CMZs into Sift, and then create and calculate some queries. We will simply use the results from Sifts built in Auto Populate Queries dialog. Your load and explore pages should look as follows:

If you are having trouble with the above instructions, the Sift Tutorials wiki page has many tutorials that will help you out.

(HipAngleX - Traces: 2 distinct groups and several outliers) (also HipAngleZ from auto builder)