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

## Documentation for package ‘clusternor’ version 0.0-4

- DESCRIPTION file.

## Help Pages

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