Event Clustering Event Series Characterization on Expected Frequency
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We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of timestamps. Given an expected frequency Δ T^-1, we introduce an O(N)-efficient method of characterizing N events represented by an ordered series of timestamps t_1,t_2,...,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 Δ T to e.g. determine the quality of an Internet of Things service.
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