Response-Guided Community Detection: Application to Climate Index Discovery

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 9285; pp. 736 - 751
• Main Authors: Bello, Gonzalo A., Angus, Michael, Pedemane, Navya, Harlalka, Jitendra K., Semazzi, Fredrick H. M., Kumar, Vipin, Samatova, Nagiza F.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial intelligence; Climate index discovery; Community detection; Data mining; Pattern recognition; Seasonal rainfall prediction; Spatiotemporal data
• ISBN: 9783319235240; 3319235249
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-23525-7_45

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.