We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of timestamps. Given an expected frequency $Delta T^{-1}$, we introduce an $mathcal{O}(N)$-efficient method of characterizing $N$ events represented by an ordered series of timestamps $t_1,t_2,dots,t_N$. In practice, the method proves useful to e.g. identify time intervals of missing data or to locate isolated events. Moreover, we define measures to quantify a series of events by varying $Delta T$ to e.g. determine the quality of an Internet of Things service.
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