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--- sift:tutorials:tutorial_overview [2025/02/06 19:14] – sgranger
+++ sift:tutorials:tutorial_overview [2025/05/21 16:14] (current) – Added links to "Using K-means to cluster kinetic features in above-knee amputees" and reorganized PCA tutorial section. wikisysop
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   * **[[Sift:Tutorials:Perform_Principal_Component_Analysis|Perform Principal Component Analysis]]**: This tutorial provides an overview of performing PCA. This tutorial is the same as the PCA tutorial in the "Getting Started" section.
+Once PCA has been performed, it is possible to run different quality assurance processes:
+  * **[[Sift:Tutorials:Outlier_Detection_with_PCA|PCA Outlier Detection]]**: This tutorial shows how you can use outlier detection methods to find outliers from your PCA analysis.
   * **[[Sift:Tutorials:Run_K-Means|Run K-Means]]**: This tutorial shows how you can use the k-means algorithms to cluster the results of PCA analysis.
-  * **[[Sift:Tutorials:Outlier_Detection_with_PCA|PCA Outlier Detection]]**: This tutorial shows how you can use outlier detection methods to find outliers from your PCA analysis.
+  * **[[sift:tutorials:using_kmeans_to_cluster_kinetic_features_in_above_the_knee_amputees|Using K-means to cluster kinetic features in above-knee amputees]]**: This tutorial shows how you can use the k-means algorithm to explore a public data set.
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   * **[[sift:tutorials:command_line|Batch Processing through the Command Line]]**: This tutorial demonstrates how Sift's command line interface can be used to automate analysis tasks and how these tasks can be automated using the Windows operating system.
+  * **[[sift:tutorials:using_directory_watchers| Automating Work Flow With Directory Watchers]]**: This tutorial demonstrates how Sift's directory watchers can be used to automate an entire processing pipeline via the command line.
-==== Directory Watchers ====
-  * **[[sift:tutorials:using_directory_watchers| Automating Work Flow With Directory Watchers]]**: This tutorial demonstrates how Sift's directory watchers can be used to automate an entire processing pipeline.
 ==== Public Data Sets ====
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   * **[[Sift:Tutorials:OpenBiomechanics_Project:Analysis_of_Baseball_Hitters_at_Different_Levels_of_Competition|Analysis of Baseball Hitters at Different Levels of Competition]]**: This tutorial shows how you can use Sift to automate the processing of large-scale data sets, and how metadata can be used to help you refine queries.
   * **[[Sift:Tutorials:OpenBiomechanics_Project:Analysis_of_Shoulder_Angular_Velocity_Baseball_Pitching|Analysis of Shoulder Angular Velocity between Elite Level and Average Collegiate Pitchers]]**: This tutorial shows you how to compare two groups using the normal database feature.
+  * **[[sift:tutorials:using_kmeans_to_cluster_kinetic_features_in_above_the_knee_amputees|Using K-means to cluster kinetic features in above-knee amputees]]**: This tutorial shows how you can use the k-means algorithm to explore kinetic features for individuals using prosthetics after above-knee amputation.