Description Usage Arguments Value Examples
orthoKMeansTrain
will cluster a given data set into the specified number of
clusters. It can use either random initialization of the centroids or use KMeans++ for this.
The K-Means training itself is accelerated by using techniques by Greg Hamerly.
Orthoginality is implemented by using ideas from Cui et al 'Non-redudant multi-view
clustering via orthogonalization'.
1 2 | orthoKMeansTrain(x = NULL, k = NULL, rounds = 1, iter.max = 100,
init.type = "KMeans++", verbose = FALSE)
|
x |
data to cluster |
k |
number of centroids |
rounds |
number of rounds/views for orthogonal kmeans |
iter.max |
number of maximal iterations for each clustering |
init.type |
string with method to initialize centroids |
verbose |
show verbose messages? |
an S3 object containing the cluster labels for the training set as well as all necessary information for prediction.
1 2 |
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