Sift Tutorial: Run K-Means

When analyzing bio-mechanical signals, we often realize that a number of individual traces are similar. It can be useful to describe these traces as belonging to the same group, or cluster. This potentially allows us to simplify our analysis or to pick a single trace as being "representative" of the whole cluster. Because clustering is an unsupervised learning technique, it does not require any specific knowledge or set of training labels from the user. This, in turn makes clustering useful for data exploration. For more information check out the K-means clustering page. This tutorial will showcase how to interpret the results of clustering between a normal group and an osteoarthritis group.

Overview of Tutorial

This tutorial works off the Principal Component Analysis Tutorial, and assumes a good understanding of using PCA in Sift. This tutorial uses overground walking data from roughly 100 subjects divided into two conditions, normal control and osteoarthritis (moderate to severe). This data set is included in the Demo folder of you Sift installation (e.g., C:\Program Files\Sift\Demo). This is the same data as the PCA Tutorial.