Home  | Publications | KEK+19

Detecting Global Periodic Correlated Clusters in Event Series Based on Parameter Space Transform

MCML Authors

Peer Kröger

Prof. Dr.

Principal Investigator

* Former Principal Investigator

Link to Profile Thomas Seidl PI Matchmaking

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

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.

inproceedings

SSDBM 2019

31st International Conference on Scientific and Statistical Database Management. Santa Cruz, CA, USA, Jul 23-25, 2019.

Authors

D. Kazempour • K. Emmerig • P. Kröger • T. Seidl

Links

DOI

Research Area

 A3 | Computational Models

BibTeXKey: KEK+19

Back to Top