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) <doi:10.1093/bioinformatics/btaa593>.
|Author||Alessandra Cabassi [aut, cre] (<https://orcid.org/0000-0003-1605-652X>), Paul DW Kirk [ths] (<https://orcid.org/0000-0002-5931-7489>), Mehmet Gonen [ctb] (<https://orcid.org/0000-0002-2483-075X>)|
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
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