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Kazempour, Daniyal; Emmerig, Kilian; Kröger, Peer; Seidl, Thomas (2019): Detecting Global Periodic Correlated Clusters in Event Series based on Parameter Space Transform. In: Scientific and Statistical Database Management (Ssdbm 2019): pp. 222-225
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Abstract

Periodicities are omnipresent: In nature in the cycles of predator and prey populations, reoccurring patterns regarding our power consumption over the days, or the presence of flu diseases over the year. With regards to the importance of periodicities we ask: Is there a way to detect periodic correlated clusters which are hidden in event series? We propose as a work in progress a method for detecting sinusoidal periodic correlated clusters on event series which relies on parameter space transformation. Our contributions are: Providing the first non-linear correlation clustering algorithm for detecting periodic correlated clusters. Further our method provides an explicit model giving domain experts information on parameters such as amplitude, frequency, phase-shift and vertical-shift of the detected clusters. Beyond that we approach the issue of determining an adequate frequency and phase-shift of the detected correlations given a frequency and phase-shift boundary.