  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.
