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klic
View on CRAN: Click
here
Download and install klic package within the R console
Install from CRAN:
install.packages("klic")
Install from Github:
library("remotes")
install_github("cran/klic") Install by package version:
library("remotes")
install_version("klic", "1.0.4") Attach the package and use:
library("klic")
Maintained by
Alessandra Cabassi
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All associated links for this package
First Published: 2020-04-03
Latest Update: 2020-07-06
Description:
Kernel Learning Integrative Clustering (KLIC) is an algorithm that allows to combine multiple kernels, each representing a different measure of the similarity between a set of observations. The contribution of each kernel on the final clustering is weighted according to the amount of information carried by it. As well as providing the functions required to perform the kernel-based clustering, this package also allows the user to simply give the data as input: the kernels are then built using consensus clustering. Different strategies to choose the best number of clusters are also available. For further details please see Cabassi and Kirk (2020) .
How to cite:
Alessandra Cabassi (2020). klic: Kernel Learning Integrative Clustering. R package version 1.0.4, https://cran.r-project.org/web/packages/klic. Accessed 04 Jun. 2026.
Previous versions and publish date:
1.0.2 (2020-04-03 18:00)
Other packages that cited klic R package
View klic citation profile
Other R packages that klic depends,
imports, suggests or enhances
Complete documentation for klic
Functions, R codes and Examples using
the klic R package
Some associated functions: copheneticCorrelation . kkmeans . klic . lmkkmeans . lmkkmeans_missingData . plotSimilarityMatrix . spectrumShift .
Some associated R codes: cophenetic-correlation.R . kkmeans.R . klic.R . lmkkmeans.R . lmkkmeans_missingData.R . plot-similarity-matrix.R . spectrum-shift.R . Full klic package functions and examples
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