k-Means Clustering
The k-Means Clustering method differs from the Heat Map method since it groups data points by partitioning them into a fixed number of arbitrary groups and then repeatedly refining the groups. This process is done by first randomly selecting one starting point for each cluster, and then grouping each of the data points to the closest starting point. The algorithm then defines a new center point for each group by finding the centroid, and each data point is then re-grouped to the closest center point. This process is repeated again and again, until the process no longer yields improvement.
k-Means Clustering may be:
Displayed in Line Graph Thumbnails, as shown below Displayed in Heat Map formats Performed as single trial, or multiple trials may be run on one set of data points (performing multiple trials will create multiple sets of clusters) Performed on either the entire gene set or on a selected subset
Each individual Line Graph shows a visualization of the data contained within one cluster. Expression Levels are shown plotted against each experiment in your project.
Additional details on the Line Graph Thumbnail image of the k-Means cluster can be obtained by clicking here.
As with the Heat Map method, k-Means clustering results can be saved, and accessed later. This allows management of clustering results, as well as the option to display your clustering result in another view.