Skmeans {clusternor} | R Documentation |
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.
Skmeans(data, centers, nrow = -1, ncol = -1, iter.max = .Machine$integer.max, nthread = -1, init = c("kmeanspp", "random", "forgy", "none"), tolerance = 1e-06)
data |
Data file name on disk (NUMA optmized) or In-memory data matrix |
centers |
Either (i) The number of centers (i.e., k), or (ii) an In-memory data matrix |
nrow |
The number of samples in the dataset |
ncol |
The number of features in the dataset |
iter.max |
The maximum number of iteration of k-means to perform |
nthread |
The number of parallel threads to run |
init |
The type of initialization to use c("kmeanspp", "random", "forgy", "none") |
tolerance |
The convergence tolerance |
A list containing the attributes of the output. cluster: A vector of integers (from 1:k) indicating the cluster to which each point is allocated. centers: A matrix of cluster centres. size: The number of points in each cluster. iter: The number of (outer) iterations.
Disa Mhembere <disa@cs.jhu.edu>
iris.mat <- as.matrix(iris[,1:4]) k <- length(unique(iris[, dim(iris)[2]])) # Number of unique classes km <- Skmeans(iris.mat, k)