This repository contains Matlab and R implementations of the clustering algorithms in "Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology", which is appearing in Advances in Neural Information Processing Systems 27 (NIPS 2014).

demo_kernel_kmeans.m file shows how to use the kernel k-means clustering algorithm in Matlab.
demo_kernel_kmeans.R file shows how to use the kernel k-means clustering algorithm in R.
demo_multiple_kernel_kmeans.m file shows how to use the multiple kernel k-means clustering algorithm in Matlab.
demo_multiple_kernel_kmeans.R file shows how to use the multiple kernel k-means clustering algorithm in R.
demo_localized_multiple_kernel_kmeans.m file shows how to use the localized multiple kernel k-means clustering algorithm in Matlab.
demo_localized_multiple_kernel_kmeans.R file shows how to use the localized multiple kernel k-means clustering algorithm in R.

clustering methods
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* kkmeans_train.m => training procedure for kernel k-means in Matlab
* kkmeans_train.R => training procedure for kernel k-means in R
* mkkmeans_train.m => training procedure for multiple kernel k-means in Matlab (requries Mosek optimization software)
* mkkmeans_train.R => training procedure for multiple kernel k-means in R (requries Mosek optimization software)
* lmkkmeans_train.m => training procedure for localized multiple kernel k-means in Matlab (requries Mosek optimization software)
* lmkkmeans_train.R => training procedure for localized multiple kernel k-means in R (requries Mosek optimization software)

If you use any of the algorithms implemented in this repository, please cite the following paper:

Mehmet Gonen and Adam A. Margolin. Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology. Advances in Neural Information Processing Systems 27 (NIPS 2014), Montréal, Québec, Canada, 2014.