A Parallel Clustering Non-Uniform Memory Access ('NUMA') Optimized Package

Documentation for package ‘clusternor’ version 0.0-4

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.
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.
Xmeans	Perform a parallel hierarchical clustering using the x-means algorithm