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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)

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