From neuroscience and genomics to systems biology and ecology, researchers rely on clustering similarity data to uncover modular structure. Most methods work in two steps: first reduce noise, then cluster. The first step runs without regard to the second: it forces arbitrary choices, discards useful information, and can impose structure where none exists. Here we bring the two steps together. Using established Bayesian community detection methods, we let one minimum description length criterion choose both how much to sparsify the data and how to cluster it. This one-step approach keeps all the samples for inference, removes the arbitrary threshold choice, and returns a single cluster when there is no real structure. On synthetic data, our approach recovers clusters under more noise and with fewer samples than standard methods. On gene co-expression data from the human brain, it finds more coherent and more stable clusters than WGCNA, the standard tool in the field. Uniting sparsification and clustering in a single model-selection step provides a principled, noise-resistant way to uncover modular structure in similarity data across fields.

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