Compressed Hierarchical Clustering.
hcc_parallel(
Data,
W,
K,
maxLevel,
ncores = 2,
DIR_output = tempfile(),
hybrid = FALSE,
verbose = FALSE,
...
)
A Filebacked Big Matrix n x N. Data signals are stored in the matrix columns.
A frequency matrix m x n with frequency vectros in rows.
Number of clusters at each call of the clustering algorithm.
Maximum number of hierarchical levels.
Number of cores. By default 4.
An output directory.
logical parameter. If TRUE K
decreases progressively over
hierarchical levels as
logical that indicates whether dysplay the processing steps.
Additional arguments passed on to COMPR
.
The cluster assignment as a list of clusters with corresponding data vector indeces.
This function provides a divisive hierarchical implementation of COMPR
.
Parallel computations are performed using 'FORK' clusters (Linux-like platform) or 'PSOCK' clusters (Windows platform) using the parallel
package.
This function generates in the DIR_output
directory the following files:
'Cluster_assign_out.bk' is a Filebacked Big Matrix N x maxLevel
+1, which stores the cluster assignment at each hierarchical level.
'Centroids_out.bk' is a Filebacked Big Matrix with the resulting cluster centroids in columns.
DBLP:journals/corr/KerivenTTG16chickn
# NOT RUN {
data("UPS2")
N = ncol(UPS2)
n= nrow(UPS2)
X_FBM = bigstatsr::FBM(init = UPS2, ncol=N, nrow = n)$save()
K_W1 = Nystrom_kernel(Data = X_FBM, c = 14, l = 7, s = 5,
max_neighbors = 3, ncores = 1, kernel = 'Gaussian')$K_W1
W = GenerateFrequencies(Data = K_W1, m = 20, N0 = ncol(X_FBM))$W
C = hcc_parallel(Data = K_W1, W = W, K = 2, maxLevel = 4,
DIR_output = tempfile(), ncores = 2)
# }
Run the code above in your browser using DataLab