Event Clustering & Event Series Characterization on Expected Frequency

We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of timestamps. Given an expected frequency $ΔT^{-1}$, 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 $ΔT$ to e.g. determine the quality of an Internet of Things service.

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