As second-generation gravitational-wave detectors prepare to analyze data at unprecedented sensitivity, there is great interest in searches for unmodeled transients, commonly called bursts. Significant effort has yielded a variety of techniques to identify and characterize such transient signals, and many of these methods have been applied to produce astrophysical results using data from first-generation detectors. However, the computational cost of background estimation remains a challenging problem; it is difficult to claim a $5\sigma$ detection with reasonable computational resources without paying for efficiency with reduced sensitivity. We demonstrate a hierarchical approach to gravitational-wave transient detection, focusing on long-lived signals, which can be used to detect transients with significance in excess of $5\sigma$ using modest computational resources. In particular, we show how previously developed seedless clustering techniques can be applied to large data sets to identify high-significance candidates without having to trade sensitivity for speed.

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