| Title: | Alternating K-Means Biclustering |
| Version: | 0.1.0 |
| Description: | Implements the alternating k-means biclustering algorithm in Fraiman and Li (2020) <doi:10.48550/arXiv.2009.04550>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | no |
| Packaged: | 2020-09-18 17:38:42 UTC; lizichao |
| Author: | Zichao Li [aut, cre], Nicolas Fraiman [aut] |
| Maintainer: | Zichao Li <lizichao@live.unc.edu> |
| Repository: | CRAN |
| Date/Publication: | 2020-09-24 09:10:15 UTC |
Alternating k-means biclustering
Description
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.
Usage
akmbiclust(X, k, lambda = 0, nstart = 1)
Arguments
X |
Data matrix. |
k |
The number of biclusters. |
lambda |
Regularization parameter. Default is 0. |
nstart |
The number of random initializations. Default is 1. |
Value
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. |
Author(s)
Nicolas Fraiman and Zichao Li
References
N. Fraiman and Z. Li (2020). Biclustering with Alternating K-Means. arXiv preprint arXiv:2009.04550.
Examples
# 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