Using Multivariate Clustering to Characterize Ecoregion Borders
Computing in Science and Engineering
Discovery of climate indices using clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Spatio-Temporal Patterns in Climate Data Using Clustering
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
Introduction to Environmental Geology (4th Edition)
Introduction to Environmental Geology (4th Edition)
Geographic analysis & visualization of climate extremes for the Quadrennial Defense Review
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Discovery of extreme events-related communities in contrasting groups of physical system networks
Data Mining and Knowledge Discovery
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To discover patterns in historical data, climate scientists have applied various clustering methods with the goal of identifying regions that share some common climatological behavior. However, past approaches are limited by the fact that they either consider only a single time period (snapshot) of multivariate data, or they consider only a single variable by using the time series data as multi-dimensional feature vector. In both cases, potentially useful information may be lost. Moreover, clusters in high-dimensional data space can be difficult to interpret, prompting the need for a more effective data representation. We address both of these issues by employing a complex network (graph) to represent climate data, a more intuitive model that can be used for analysis while also having a direct mapping to the physical world for interpretation. A cross correlation function is used to weight network edges, thus respecting the temporal nature of the data, and a community detection algorithm identifies multivariate clusters. Examining networks for consecutive periods allows us to study structural changes over time. We show that communities have a climatological interpretation and that disturbances in structure can be an indicator of climate events (or lack thereof). Finally, we discuss how this model can be applied for the discovery of more complex concepts such as unknown teleconnections or the development of multivariate climate indices and predictive insights.