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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper generalizes multi-kernel k-means clustering (solved via relaxation) to the case where each clustered item (here, a person) gets an item-specific set of weights over the multiple kernels, rather than the traditional, shared, global weighting of the kernels. Using TCGA (cancer) data, with 3 modalities, they demonstrate that this generalization yields better clusterings than the traditional (global approach), when measured against 3 bronze standard clusterings arising from known clinical clusters. The writing is clear, making for an easy read. Although this is a somewhat incremental-seeming tweak, I think it was clever, with the potential to actually be used (rather than lost in the NIPS archives), and therefore of some significance. Other comments: In the introduction you mention that k-means is susceptible to local minima, and then use this to motivate the relaxation approach.