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Clustering Dynamic Spatio-Temporal Patterns in The Presence of Noise and Missing Data

Xi C. Chen, James H. Faghmous, Ankush Khandelwal, Vipin Kumar

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Computational Sustainability. Pages 2575-2580.

Abstract

Clustering has gained widespread use, especially for static data. However, the rapid growth of spatio-temporal data from numerous instruments, such as earth-orbiting satellites, has created a need for spatio-temporal clustering methods to extract and monitor dynamic clusters. Dynamic spatio-temporal clustering faces two major challenges: First, the clusters are dynamic and may change in size, shape, and statistical properties over time. Second, numerous spatio-temporal data are incomplete, noisy, heterogeneous, and highly variable (over space and time). We propose a new spatio-temporal data mining paradigm, to autonomously identify dynamic spatio-temporal clusters in the presence of noise and missing data. Our proposed approach is more robust than traditional clustering and image segmentation techniques in the case of dynamic patterns, non-stationary, heterogeneity, and missing data. We demonstrate our method's performance on a real-world application of monitoring in-land water bodies on a global scale.

Xi C. Chen, James H. Faghmous, Ankush Khandelwal, and Vipin Kumar. 2015. Clustering Dynamic Spatio-Temporal Patterns in The Presence of Noise and Missing Data. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 2575–2580. https://ijcai.org/proceedings/2015/365
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