Skmeans: Perform spherical k-means clustering on a data matrix....

In neurodata/knorR: A Parallel Clustering Non-Uniform Memory Access ('NUMA') Optimized Library

Description

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.

Usage

Skmeans(data, centers, nrow = -1, ncol = -1,
  iter.max = .Machine$integer.max, nthread = -1, init = c("kmeanspp",
  "random", "forgy", "none"), tolerance = 1e-06)

Arguments

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

Value

A list containing the attributes of the output of kmedoids. 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.

Author(s)

Disa Mhembere <[e-mail]>

Examples

iris.mat <- as.matrix(iris[,1:4])
k <- length(unique(iris[, dim(iris)[2]])) # Number of unique classes
km <- Skmeans(iris.mat, k)

neurodata/knorR documentation built on May 25, 2019, 10:35 p.m.