An exploration of climate data using complex networks

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 algori...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. Ganguly
• Keywords: Data Mining, KDD 2009, Multivariate Data, Time Series Data, Various Clustering Methods