Discovery of climate indices using clustering

Michael S Steinbach, Pang Ning Tan, Vipin Kumar, Steven Klooster, Christopher Potter

Research output: Contribution to conferencePaperpeer-review

113 Scopus citations


To analyze the effect of the oceans and atmosphere on land climate, Earth Scientists have developed climate indices, which are time series that summarize the behavior of selected regions of the Earth's oceans and atmosphere. In the past, Earth scientists have used observation and, more recently, eigenvalue analysis techniques, such as principal components analysis (PCA) and singular value decomposition (SVD), to discover climate indices. However, eigenvalue techniques are only useful for finding a few of the strongest signals. Furthermore, they impose a condition that all discovered signals must be orthogonal to each other, making it difficult to attach a physical interpretation to them. This paper presents an alternative clustering-based methodology for the discovery of climate indices that overcomes these limitiations and is based on clusters that represent regions with relatively homogeneous behavior. The centroids of these clusters are time series that summarize the behavior of the ocean or atmosphere in those regions. Some of these centroids correspond to known climate indices and provide a validation of our methodology; other centroids are variants of known indices that may provide better predictive power for some land areas; and still other indices may represent potentially new Earth science phenomena. Finally, we show that cluster based indices generally outperform SVD derived indices, both in terms of area weighted correlation and direct correlation with the known indices.

Original languageEnglish (US)
Number of pages10
StatePublished - 2003
Event9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 - Washington, DC, United States
Duration: Aug 24 2003Aug 27 2003


Conference9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
Country/TerritoryUnited States
CityWashington, DC


  • Clustering
  • Earth science data
  • Mining scientific data
  • Singular value decomposition
  • Time series


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