Clustering toolbox

Toolbox for cluster analysis of data ensembles

Please find toolbox to perform cluster analysis using k-means algorithm. The toolbox implements the following pipline:
1. Denoising of raw-data prior to cluster analysis, using Empirical Mode Decomposition
2. Determining number of clusters using Stability Index, a bootstrap-based method
3. Initialise cluster centroids for k-means using method based on Genetic Algorithms
4. Visualisation of cluster analysis results using PCA-based method
Simplest to run clustering_pipeline_masterscript.m, from which all other functions are called.
Read attached Usage Notes for guidance on use. Originally developed for analysis of EEG (Electroencephalographic) data, but also generally applicable. For details and example application, refer:

Williams NJ, Nasuto SJ, Saddy, JD. Method for exploratory cluster analysis and visualisation
of single-trial ERP ensembles. Journal of Neuroscience Methods 250, 22-33.

http://www.sciencedirect.com/science/article/pii/S016502701500059X

Please also cite above paper when publishing results from applying this code! The pipeline also uses/implements methods from other papers, which have been cited in the code. Please also cite these papers as appropriate.

Any feedback/comments welcome.

Hope useful!