FuzzyCMeans {clusternor} | R Documentation |
See: https://en.wikipedia.org/wiki/Fuzzy_clustering#Fuzzy_C-means_clustering
FuzzyCMeans(data, centers, nrow = -1, ncol = -1, iter.max = .Machine$integer.max, nthread = -1, fuzz.index = 2, init = c("forgy", "none"), tolerance = 1e-06, dist.type = c("sqeucl", "eucl", "cos", "taxi"))
data |
Data file name on disk (NUMA optimized) 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 |
fuzz.index |
The fuzziness coefficient/index (> 1 and < inf) |
init |
The type of initialization to use c("forgy", "none") |
tolerance |
The convergence tolerance |
dist.type |
What dissimilarity metric to use |
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. contrib.mat: The data point to cluster contribution matrix
Disa Mhembere <disa@cs.jhu.edu>
iris.mat <- as.matrix(iris[,1:4]) k <- length(unique(iris[, dim(iris)[2]])) # Number of unique classes fcm <- FuzzyCMeans(iris.mat, k, iter.max=5)