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It is a difficult task to sift though such a large collection of varied clusterings to determine which clustering features are affected by parameter settings or are artifacts of particular algorithms and which represent meaningful patterns. Knowing which items are often clustered together helps to improve our understanding of the underlying data and to increase our confidence about generated modules.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We present Coral, an application for interactive exploration of large ensembles of clusterings. Coral makes all-to-all clustering comparison easy, supports exploration of individual clusterings, allows tracking modules across clusterings, and supports identification of core and peripheral items in modules. We discuss how each visual component in Coral tackles a specific question related to clustering comparison and provide examples of their use. We also show how Coral could be used to visually and quantitatively compare clusterings with a ground truth clustering.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>As a case study, we compare clusterings of a recently published protein interaction network of<jats:italic>Arabidopsis thaliana<\/jats:italic>. We use several popular algorithms to generate the network\u2019s clusterings. We find that the clusterings vary significantly and that few proteins are consistently co-clustered in all clusterings. 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