This blog post implements a basic k-means clustering algorithm, which can be applied to arbitrary sized vectors. The clusters will be displayed graphically using a 3d plot once the calculation button is clicked (using a great library from Plotly, where you can zoom and turn the plot amongst other handy capabilities). This will be useful for visualising the spatial clustering of vectors of size 1 to 3 inclusive - the centroids are higlighted in black, while each cluster has its own colour.

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.

Alternatively you can choose to load a CSV file, that must contain only comma separated numbers.

To perform the k-means clustering, please enter the number of clusters in the appropriate field, then press the button labelled "Perform k-means clustering" below - the results will populate the textareas below labelled "Label and data sample" and "Label and Centroid values". In addition a 3-d graph plot will appear.

Note that the k-means algorithm can converge to a local minimum, and also exhibit degeneracy, whereby one of the clusters has no members, although the kmeans++ initialisation should somewhat mitigate this. Should these scenarios occur, simply re-run the algorithm, by clicking on the button labelled "Perform k-means clustering".

An example three-dimensional dataset has been loaded, with three clusters of 200 samples, as guidance.