Sift Tutorial: Outlier Detection with PCA: Difference between revisions
No edit summary |
No edit summary |
||
| Line 13: | Line 13: | ||
[[File:Sift_LOF_Tutorial_LoadPage.png|500px|thumb|The Load Page]] | [[File:Sift_LOF_Tutorial_LoadPage.png|500px|thumb|The Load Page]] | ||
[[File:Sift_LOF_Tutorial_ExplorePage.png|thumb | [[File:Sift_LOF_Tutorial_ExplorePage.png|500px|thumb|The Explore Page]] | ||
If you are having trouble with the above instructions, the [[Sift - Tutorials| Sift Tutorials]] wiki page has many tutorials that will help you out. | If you are having trouble with the above instructions, the [[Sift - Tutorials| Sift Tutorials]] wiki page has many tutorials that will help you out. | ||
Revision as of 14:01, 1 May 2024
| Language: | English • français • italiano • português • español |
|---|
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 (load from the V3D Workshop folder), 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)