## KMeans clustering

KMeans clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. Its extension to Soft-KMeans allows to have soft assignment to the clusters, which in turns allows fuzzy boundaries.
More information on Wikipedia.
Parameters:

- Clusters: number of clusters to optimize
- Method: choice of hard assignment (KMeans) or soft ones (Soft-KMeans)
- Metric: type of metric to be used

- L0: infinite norm
- L1-p: norm of degree 1-p

- Power: power for Lp norm
- Beta: boundary stiffness (Soft KMeans)