Xmeans {clusternor} | R Documentation |
A recursive (not acutally implemented as recursion) partitioning of data into two disjoint sets at every level as described in: http://cs.uef.fi/~zhao/Courses/Clustering2012/Xmeans.pdf
Xmeans(data, kmax, nrow = -1, ncol = -1, iter.max = 20, nthread = -1, init = c("forgy"), tolerance = 1e-06, dist.type = c("eucl", "cos", "taxi"), min.clust.size = 1)
data |
Data file name on disk (NUMA optmized) or In memory data matrix |
kmax |
The maximum number of centers |
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("forgy") or initial centers |
tolerance |
The convergence tolerance for k-means at each hierarchical split |
dist.type |
What dissimilarity metric to use |
min.clust.size |
The minimum size of a cluster when it cannot be split |
A list of lists 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]) kmax <- length(unique(iris[, dim(iris)[2]])) # Number of unique classes xms <- Xmeans(iris.mat, kmax)