Co-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data

International Journal of Geographical Information Science

Volumn 29, Issue 4, 2015, Pages 624-642

  Wu, Xiaojing ^a   Zurita Milla, Raul ^a   Kraak, Menno Jan ^a  

^a UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

co clustering; geo referenced time series; geovisualization; spatio temporal pattern; temperature

Indexed keywords

AIR TEMPERATURE; ALGORITHM; GIS; SPATIOTEMPORAL ANALYSIS; TIME SERIES; VISUALIZATION;

NETHERLANDS;

EID: 84930041696     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2014.994520     Document Type: Article

References (30)

1. Anagnostopoulos, A., Dasgupta, A., and Kumar, R., 2008. Approximation algorithms for co-clustering. In: Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems. New York: ACM, 201–210.
2. Andrienko, G., et al. 2009. Interactive visual clustering of large collections of trajectories. In: IEEE symposium on visual analytics science and technology (VAST), 12–13October, Atlantic City, NJ. New York: IEEE, 3–10.
3. Andrienko, G., et al., 2010a. Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns. Computer Graphics Forum, 29 (3), 913–922. doi:10.1111/j.1467-8659.2009.01664.x
4. Andrienko, G., et al., 2010b. Space, time and visual analytics. International Journal of Geographical Information Science, 24 (10), 1577–1600. doi:10.1080/13658816.2010.508043
5. Banerjee, A., et al., 2007. A generalized maximum entropy approach to bregman co-clustering and matrix approximation. Journal of Machine Learning Research, 8, 1919–1986.
6. Cheng, Y. and Church, G.M., 2000. Biclustering of expression data. Proceedings ISMB, 93–103.
7. Cho, H., et al., 2004. Minimum sum-squared residue co-clustering of gene expression data. In: Fourth SIAM international conference on data mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 114–125.
8. Coltekin, A., et al., 2011. Modifiable temporal unit problem. In: ISPRS/ICA workshop “Persistent problems in geographic visualization” (ICC2011), Paris. Berlin: Springer-Verlag.
9. Crane, R.G. and Hewitson, B.C., 2003. Clustering and upscaling of station precipitation records to regional patterns using self-organizing maps (SOMs). Climate Research, 25, 95–107. doi:10.3354/cr025095
10. Deng, M., et al., 2011. A general method of spatio-temporal clustering analysis. Science China Information Sciences, 56 (10), 1–14. doi:10.1007/s11432-011-4391-8
11. Dhillon, I.S., Mallela, S., and Modha, D.S., 2003. Information-theoretic co-clustering. In: The 9th international conference on knowledge discovery and data mining (KDD), 24–27 August, Washington, DC. New York: ACM, 89–98.
12. Dykes, J., MacEachren, A.M., and Kraak, M.-J., 2005. Exploring geovisualization. Amsterdam: Elsevier.
13. Guo, D., et al., 2006. A visualization system for space-time and multivariate patterns (VIS-STAMP). IEEE Transactions on Visualization and Computer Graphics, 12 (6), 1461–1474. doi:10.1109/TVCG.2006.84
14. Hagenauer, J. and Helbich, M., 2013. Hierarchical self-organizing maps for clustering spatiotemporal data. International Journal of Geographical Information Science, 27 (10), 2026–2042. doi:10.1080/13658816.2013.788249
15. Hagerstand, T., 1970. What about people in regional science. Papers of the Regional Science Association, 14, 7–21.
16. Han, J., Kamber, M., and Pei, J., 2012. Data mining concepts and techniques. 3rd ed. Waltham, MA: Morgan Kaufman, MIT Press.
17. Han, J., Lee, J.-G., and Kamber, M., 2009. An overview of clustering methods in geographic data analysis. In: H.J. Miller and J. Han, eds. Geographic data mining and knowledge discovery. 2nd ed. New York: Taylor & Francis Group, 150–187.
18. Jong, R.D. and Bruin, S.D., 2012. Linear trends in seasonal vegetation time series and the modifiable temporal unit problem. Biogeosciences, 9, 71–77. doi:10.5194/bg-9-71-2012
19. Keim, D.A. and Kriegel, H.-P., 1996. Visualization techniques for mining large databases: a comparison. IEEE Transcations on Knowledge and Data Engineering, 8 (6), 923–938.
20. Kisilevich, S., Mansmann, F., and Nanni, M., 2010. Spatio-temporal clustering. In: Data mining and knowledge discovery handbook. 2nd ed. New York: Springer Press, 855–874.
21. Li, S.-T. and Chou, S.-W., 2000. Multi-resolution spatio-temporal data mining for the study of air pollutant regionalization. In: The 33rd Hawaii international conference on system sciences, Maui, HI. Los Alamitos, CA: IEEE Computer Society, p. 7.
22. Li, X., et al., 2012. Explore multivariable spatio-temporal data with the time wave: case study on meteorological data. In: A.G.O. Yeh et al., ed. Advances in spatial data handling and GIS. Lecture notes in geoinformation and cartography, part 3. Berlin: Springer, 79–92. doi:10.1007/978-3-642-25926-5_7
23. Maceachren, A., et al., 2003. Exploring high-D spaces with multiform matrices and small multiples.In: IEEE Symposium on Information Visualization, INFOVIS 2003. New York: IEEE, 31–38.
24. Miller, H.J. and Han, J., 2009. Geographic data mining and knowledge discovery: an overview. In: H.J. Miller and J. Han, eds. Geographic data mining and knowledge discovery. 2nd ed. London: Taylor & Francis Group, 1–26.
25. Wu, T., et al., 2008. Mining geographic episode association patterns of abnormal events in global earth science data. Science in China Series E: Technological Sciences, 51 (S1), 155–164. doi:10.1007/s11431-008-5008-3
26. Wu, X., Zurita-Milla, R., and Kraak, M.-J., 2013. Visual discovery of synchronisation in weather data at multiple temporal resolutions. The Cartographic Journal, 50 (3), 247–256. doi:10.1179/1743277413Y.0000000067
27. Zhang, P., et al., 2003. Correlation analysis of spatial time series datasets: a filter-and-refine approach. In: K.-Y. Whang et al., eds. Advances in knowledge discovery and data mining. Berlin: Springer, 532–544.
28. Zhao, J., Forer, P., and Harvey, A.S., 2008. Activities, ringmaps and geovisualization of large human movement fields. Information Visualization, 7 (3–4), 198–209. doi:10.1057/palgrave.ivs.9500184
29. Zhou, J. and Ashfaq, K., 2006. ParRescue: scalable parallel algorithm and implementation for biclustering over large distributed datasets. In: 26th IEEE international conference on distributed computing system (ICDCS 2006). New York: IEEE, p. 21.
30. Zurita-Milla, R., et al., 2013. Exploring spatiotemporal phenological patterns and trajectories using self-organizing maps. IEEE Transactions on Geoscience and Remote Sensing, 51 (4), 1914–1921. doi:10.1109/TGRS.2012.2223218