Response-Guided Community Detection: Application to Climate Index Discovery
Discovering climate indices–time series that summarize spatiotemporal climate patterns–is a key task in the climate science domain. In this work, we approach this task as a problem of response-guided community detection; that is, identifying communities in a graph associated with a response variable of interest. To this end, we propose a general strategy for response-guided community detection that explicitly incorporates information of the response variable during the community detection process, and introduce a graph representation of spatiotemporal data that leverages information from multiple variables.
We apply our proposed methodology to the discovery of climate indices associated with seasonal rainfall variability. Our results suggest that our methodology is able to capture the underlying patterns known to be associated with the response variable of interest and to improve its predictability compared to existing methodologies for data-driven climate index discovery and official forecasts.
KeywordsCommunity detection Spatiotemporal data Climate index discovery Seasonal rainfall prediction
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- 6.Eaton, E., Mansbach, R.: A spin-glass model for semi-supervised community detection. In: Proc. of the 26th AAAI Conference on Artificial Intelligence, pp. 900–906. AAAI (2012)Google Scholar
- 7.Fisher, R.A.: Statistical methods for research workers. Edinburgh (1934)Google Scholar
- 11.Harenberg, S., Bello, G.A., Gjeltema, L., et al.: Community detection in large-scale networks: a survey and empirical evaluation. WIREs Comput. Stat. (1939-0068) (2014)Google Scholar
- 12.Harenberg, S., Seay, R.G., Ranshous, S., et al.: Memory-efficient query-driven community detection with application to complex disease associations. In: Proc. of the 2014 SIAM Int. Conf. on Data Mining, pp. 1010–1018. SIAM (2014)Google Scholar
- 22.Steinbach, M., Tan, P.N., Kumar, V., et al.: Discovery of climate indices using clustering. In: Proc. of the 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 446–455. ACM (2003)Google Scholar