Discovery of climate indices using clustering

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 limitations and is based on clusters that represent regions with relatively homogeneous behavior. The centroids of these clusters are time series that summarize the behavior o...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Michael Steinbach, Pang-Ning Tan, Vipin Kumar, Steven A. Klooster, Christopher Potter
• Keywords: Climate Indices, Cluster Based Indices, Data Mining, KDD 2003, SVD Derived Indices