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 |