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--- sift:tutorials:run_k-means [2024/11/05 14:23] – wikisysop
+++ sift:tutorials:run_k-means [2024/11/05 14:58] (current) – wikisysop
@@ Line 1: / Line 1: @@
 ====== Run K-Means ======
-/* This description seems to have been stolen from PCA, and is not relevant to K-Means? */
+The k-means clustering algorithm is a commonly used method for grouping //n// individual data points into //k// clusters. It does so in an unsupervised manner, iteratively selecting cluster centre points and assigning data points to a cluster. Within Sift, this is implemented onto the [[sift:application:analyse_page#workspace_scores|PC transformed data]], after a PCA analysis is done. More information about PCA can be found on our [[sift:principal_component_analysis:using_principal_component_analysis_in_biomechanics|Using PCA in Biomechanics page]].
-The k-means clustering algorithm is a commonly used method for grouping //n// individual data points into //k// clusters. It is a multi-variate statistical analysis that reduces the high-dimensional matrix of correlated, time-varying signals into a low-dimensional and statistically uncorrelated set of principal components (PCs). These PCs explain the variance found in the original signals and represent the most important features of the data, e.g., the overall magnitude or the shape of the time series at a particular point in the stride cycle. The value of each particular subject’s score for the individual PCs represents how strongly that feature was present in the data.
 ==== The utility of clustering ====
-When analysing biomechanical 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.
+When analysing biomechanical 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 k-means 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.
 ==== Tutorial Overview ====
-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 your Sift installation (e.g., C:\Program Files\Sift\Demo). This is the same data as the PCA Tutorial.
+This tutorial works off the [[sift:tutorials:perform_principal_component_analysis|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 your Sift installation (e.g., C:\Program Files\Sift\Demo). This is the same data as the PCA Tutorial.
 ==== Running a K-Means Test ====