Detecting climate change in multivariate time series data by novel clustering and cluster tracing techniques

Hardy Kremer, Stephan Günnemann, Thomas Seidl

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Climate change can be detected in several scientific domains including hydrology, meteorology, and oceanography. In this paper we describe our on-going work for detecting change in multivariate time series data from these domains. For the detection, we extract climate patterns from the data, represented by clusters of time series, and trace the clusters over time. A climate pattern is categorized as a changing pattern if it shows a similar tendency over a significant amount of time, e.g. several years. Since existing clustering and cluster tracing approaches are not suitable for time series data, we are working on novel clustering and tracing approaches specifically for this purpose.

Original languageEnglish
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Pages96-97
Number of pages2
DOIs
StatePublished - 2010
Externally publishedYes
Event10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia
Duration: 14 Dec 201017 Dec 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Country/TerritoryAustralia
CitySydney, NSW
Period14/12/1017/12/10

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