This blog post plots the total Within-cluster Sum of Squares (WSS) against the number of clusters, for the k-means algorithm. By examining how this parameter decreases with the increasing number of clusters, an intuition can be gained over how many clusters are required for the dataset. The elbow method can be used, whereby increasing the number of clusters after a certain cluster number does not significantly decrease the total WSS. For a k-means calculator with 3d plot display, you can have a look here.
The cluster centres (or centroids) are initialised using a variant of the k-means++ algorithm as proposed by David Arthur and Sergei Vassilvitski in 2007.
Please enter lines of comma separated numbers in the text areas below - after the last number in each line there must be no trailing comma. In addition, there must be no new line after the last sample. In addition, the maximum number of clusters in the appropriate field needs to be entered.
Alternatively you can choose to load a CSV file, that must contain only comma separated numbers.
To perform the k-means clustering for cluster size varying from 1 to the maximum specified number, simply press the button labelled "Perform k-means over multiple cluster numbers" below. The results for the maximum cluster number will populate the textareas below labelled "Label and data sample" and "Label and Centroid values". Most importantly, a graph plotting the varation of total WSS with cluster number will be updated.
An example three-dimensional dataset has been loaded, with three clusters of 200 samples, as guidance. The maximum number of clusters is set to 6, so that one can visualise the elbow of the graph at cluster number 3.
