| Title: | Alternating K-Means Biclustering |
|---|---|
| Description: | Implements the alternating k-means biclustering algorithm in Fraiman and Li (2020) <arXiv:2009.04550>. |
| Authors: | Zichao Li [aut, cre], Nicolas Fraiman [aut] |
| Maintainer: | Zichao Li <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-10-31 22:12:34 UTC |
| Source: | https://github.com/cran/akmbiclust |
This function uses the alternating k-means biclustering algorithm to extract the k biclusters in the matrix X. See the paper "Biclustering with Alternating K-Means" for more details.
akmbiclust(X, k, lambda = 0, nstart = 1)akmbiclust(X, k, lambda = 0, nstart = 1)
X |
Data matrix. |
k |
The number of biclusters. |
lambda |
Regularization parameter. Default is 0. |
nstart |
The number of random initializations. Default is 1. |
A list containing three objects:
row_labels |
The bicluster labels of every row. |
col_labels |
The bicluster labels of every column. |
loss |
The loss of the produced biclusters. |
Nicolas Fraiman and Zichao Li
N. Fraiman and Z. Li (2020). Biclustering with Alternating K-Means. arXiv preprint arXiv:2009.04550.
# we create a 100 by 100 matrix X which has an underlying 2 by 2 block structure. # The entries in the two 50 by 50 blocks on the top left and bottom right follow # i.i.d. normal with mean 0 and variance 4. The entries in the two 50 by 50 blocks # on the top right and bottom left follow i.i.d. normal with mean 0 and variance 1. X <- matrix(rnorm(10000, 0, 1), 100, 100) X[1:50, 1:50] <- matrix(rnorm(2500, 0, 2), 50, 50) X[51:100, 51:100] <- matrix(rnorm(2500, 0, 2), 50, 50) # Alternating k-means biclustering # Result: perfect result <- akmbiclust(X, 2, lambda = 0, nstart = 100) result$row_labels result$col_labels # Separate k-means clustering on the rows and columns # Result: random kmeans(X, 2)$cluster kmeans(t(X), 2)$cluster# we create a 100 by 100 matrix X which has an underlying 2 by 2 block structure. # The entries in the two 50 by 50 blocks on the top left and bottom right follow # i.i.d. normal with mean 0 and variance 4. The entries in the two 50 by 50 blocks # on the top right and bottom left follow i.i.d. normal with mean 0 and variance 1. X <- matrix(rnorm(10000, 0, 1), 100, 100) X[1:50, 1:50] <- matrix(rnorm(2500, 0, 2), 50, 50) X[51:100, 51:100] <- matrix(rnorm(2500, 0, 2), 50, 50) # Alternating k-means biclustering # Result: perfect result <- akmbiclust(X, 2, lambda = 0, nstart = 100) result$row_labels result$col_labels # Separate k-means clustering on the rows and columns # Result: random kmeans(X, 2)$cluster kmeans(t(X), 2)$cluster