| Type: | Package |
| Title: | Dynamic Programming for Convex Clustering |
| Version: | 1.0.0 |
| Author: | Bingyuan Zhang, Jie Chen, Yoshikazu Terada |
| Maintainer: | Bingyuan Zhang <zhang@sigmath.es.osaka-u.ac.jp> |
| Description: | Use dynamic programming method to solve l1 convex clustering with identical weights. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | False |
| RoxygenNote: | 7.1.1 |
| LinkingTo: | Rcpp |
| Imports: | Rcpp |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | yes |
| Packaged: | 2021-05-24 05:11:05 UTC; zhangbingyuan |
| Repository: | CRAN |
| Date/Publication: | 2021-06-01 06:40:02 UTC |
L1 convex clustering with a single lambda.
Description
L1 convex clustering with a single lambda.
Usage
cdp(X, lam)
Arguments
X |
a data matrix of n * p or a data vector with length n. |
lam |
a tuning parameter. |
Details
A list with length p equal to the dimension of the data matrix. Each dimension includes a vector of the estimated centroids.
Value
the estimated centroids.
Examples
# generate a data matrix with n = 10 and p = 2.
X = matrix(rnorm(10*2), 10, 2)
lam = find_lambda(X)/2
# set a tuning parameter lambda.
cdp(X, lam)
L1 convex clustering with a lambda sequence.
Description
L1 convex clustering with a lambda sequence.
Usage
cpaint(X, lam)
Arguments
X |
a data matrix of n * p or a data vector with length n. |
lam |
a sequence of lambdas. |
Details
A list with length p equal to the dimension of the data matrix. Each dimension includes a sequence of vectors. Each vector includes the estimated centroids with a certain tuning parameter lambda.
Value
A sequence of estimated centroids.
Examples
# generate a data matrix with n = 10 and p = 2.
X = matrix(rnorm(10*2), 10, 2)
# set the biggest lambda in the sequence.
lam_max = find_lambda(X)
# set the length of the sequence.
K = 10
# equally separate the sequence with K.
Lam = sapply(1:K, function(i) i/K*lam_max)
cpaint(X,Lam)
Return the lambda which causes all the points become fused into one cluster.
Description
Return the lambda which causes all the points become fused into one cluster.
Usage
find_lambda(X)
Arguments
X |
data matrix of n * p |
Value
the biggest lambda
Examples
X = matrix(rnorm(3*2), 3, 2)
find_lambda(X)