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 O(N)-efficient method of characterizing N events represented by an ordered series of timestamps t1,t2,…,tN. 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.
@article{arxiv.2004.02089,
title = {Event Clustering & Event Series Characterization on Expected Frequency},
author = {Conrad M Albrecht and Marcus Freitag and Theodore G van Kessel and Siyuan Lu and Hendrik F Hamann},
journal= {arXiv preprint arXiv:2004.02089},
year = {2020}
}