Response-guided community detection: Application to climate index discovery

Gonzalo A. Bello; Michael Angus; Navya Pedemane; Jitendra K. Harlalka; Fredrick H.M. Semazzi; Vipin Kumar; Nagiza F. Samatova

doi:10.1007/978-3-319-23525-7_45

Response-guided community detection: Application to climate index discovery

Gonzalo A. Bello, Michael Angus, Navya Pedemane, Jitendra K. Harlalka, Fredrick H.M. Semazzi, Vipin Kumar, Nagiza F. Samatova

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015
Editors	Vitor Santos Costa, Carlos Soares, Annalisa Appice, Annalisa Appice, Pedro Pereira Rodrigues, Vitor Santos Costa, Carlos Soares, João Gama, Alípio Jorge, Pedro Pereira Rodrigues, João Gama, Vitor Santos Costa, Alípio Jorge, Annalisa Appice, Pedro Pereira Rodrigues, João Gama, Annalisa Appice, Carlos Soares, Alípio Jorge, João Gama, Pedro Pereira Rodrigues, Vitor Santos Costa, Carlos Soares, Alípio Jorge
Publisher	Springer Verlag
Pages	736-751
Number of pages	16
ISBN (Print)	9783319235240, 9783319235240, 9783319235240, 9783319235240
DOIs	https://doi.org/10.1007/978-3-319-23525-7_45
State	Published - 2015
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015 - Porto, Portugal Duration: Sep 7 2015 → Sep 11 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9285
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015
Country/Territory	Portugal
City	Porto
Period	9/7/15 → 9/11/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Keywords

Climate index discovery
Community detection
Seasonal rainfall prediction
Spatiotemporal data

Publisher link

10.1007/978-3-319-23525-7_45

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Bello, G. A., Angus, M., Pedemane, N., Harlalka, J. K., Semazzi, F. H. M., Kumar, V., & Samatova, N. F. (2015). Response-guided community detection: Application to climate index discovery. In V. S. Costa, C. Soares, A. Appice, A. Appice, P. P. Rodrigues, V. S. Costa, C. Soares, J. Gama, A. Jorge, P. P. Rodrigues, J. Gama, V. S. Costa, A. Jorge, A. Appice, P. P. Rodrigues, J. Gama, A. Appice, C. Soares, A. Jorge, J. Gama, P. P. Rodrigues, V. S. Costa, C. Soares, ... A. Jorge (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015 (pp. 736-751). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9285). Springer Verlag. https://doi.org/10.1007/978-3-319-23525-7_45

Response-guided community detection: Application to climate index discovery. / Bello, Gonzalo A.; Angus, Michael; Pedemane, Navya et al.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015. ed. / Vitor Santos Costa; Carlos Soares; Annalisa Appice; Annalisa Appice; Pedro Pereira Rodrigues; Vitor Santos Costa; Carlos Soares; João Gama; Alípio Jorge; Pedro Pereira Rodrigues; João Gama; Vitor Santos Costa; Alípio Jorge; Annalisa Appice; Pedro Pereira Rodrigues; João Gama; Annalisa Appice; Carlos Soares; Alípio Jorge; João Gama; Pedro Pereira Rodrigues; Vitor Santos Costa; Carlos Soares; Alípio Jorge. Springer Verlag, 2015. p. 736-751 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9285).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Bello, GA, Angus, M, Pedemane, N, Harlalka, JK, Semazzi, FHM, Kumar, V & Samatova, NF 2015, Response-guided community detection: Application to climate index discovery. in VS Costa, C Soares, A Appice, A Appice, PP Rodrigues, VS Costa, C Soares, J Gama, A Jorge, PP Rodrigues, J Gama, VS Costa, A Jorge, A Appice, PP Rodrigues, J Gama, A Appice, C Soares, A Jorge, J Gama, PP Rodrigues, VS Costa, C Soares & A Jorge (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9285, Springer Verlag, pp. 736-751, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015, Porto, Portugal, 9/7/15. https://doi.org/10.1007/978-3-319-23525-7_45

Bello GA, Angus M, Pedemane N, Harlalka JK, Semazzi FHM, Kumar V et al. Response-guided community detection: Application to climate index discovery. In Costa VS, Soares C, Appice A, Appice A, Rodrigues PP, Costa VS, Soares C, Gama J, Jorge A, Rodrigues PP, Gama J, Costa VS, Jorge A, Appice A, Rodrigues PP, Gama J, Appice A, Soares C, Jorge A, Gama J, Rodrigues PP, Costa VS, Soares C, Jorge A, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015. Springer Verlag. 2015. p. 736-751. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-23525-7_45

Bello, Gonzalo A. ; Angus, Michael ; Pedemane, Navya et al. / Response-guided community detection : Application to climate index discovery. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015. editor / Vitor Santos Costa ; Carlos Soares ; Annalisa Appice ; Annalisa Appice ; Pedro Pereira Rodrigues ; Vitor Santos Costa ; Carlos Soares ; João Gama ; Alípio Jorge ; Pedro Pereira Rodrigues ; João Gama ; Vitor Santos Costa ; Alípio Jorge ; Annalisa Appice ; Pedro Pereira Rodrigues ; João Gama ; Annalisa Appice ; Carlos Soares ; Alípio Jorge ; João Gama ; Pedro Pereira Rodrigues ; Vitor Santos Costa ; Carlos Soares ; Alípio Jorge. Springer Verlag, 2015. pp. 736-751 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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AU - Bello, Gonzalo A.

AU - Angus, Michael

AU - Pedemane, Navya

AU - Harlalka, Jitendra K.

AU - Semazzi, Fredrick H.M.

AU - Kumar, Vipin

AU - Samatova, Nagiza F.

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

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N2 - 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.

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Response-guided community detection: Application to climate index discovery

Abstract

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Keywords

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