FuzzyCMeans             Perform Fuzzy C-means clustering on a data
                        matrix. A soft variant of the kmeans algorithm
                        where each data point are assigned a
                        contribution weight to each cluster
Hmeans                  Perform parallel hierarchical clustering on a
                        data matrix.
Kmeans                  Perform k-means clustering on a data matrix.
KmeansPP                Perform the k-means++ clustering algorithm on a
                        data matrix.
MiniBatchKmeans         A randomized dataset sub-sample algorithm that
                        approximates the k-means algorithm. See:
                        https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf
Skmeans                 Perform spherical k-means clustering on a data
                        matrix. Similar to the k-means algorithm
                        differing only in that data features are
                        min-max normalized the dissimilarity metric is
                        Cosine distance.
Xmeans                  Perform a parallel hierarchical clustering
                        using the x-means algorithm
test_centroids          A small example of centroids of dim: (8,5) used
                        as for micro-benchmarks of the clusternor
                        package. The data are randomly generated.
test_data               A small dataset of dim: (50,5) used as for
                        micro-benchmarks of the clusternor package. The
                        data are randomly generated hence a clear
                        number of clusters will be hard to find.
