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 ------------------ * 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.