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 times-tamps. Given an expected frequency ΔT⁻¹, we introduce an O(N)-efficient method of characterizing N events represented by an ordered series of timestamps t<inf>1</inf>, t<inf>2</inf>,…, t<inf>N</inf>. 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.