TY - GEN
T1 - Clustering dynamic spatio-temporal patterns in the presence of noise and missing data
AU - Chen, Xi C.
AU - Faghmous, James H.
AU - Khandelwal, Ankush
AU - Kumar, Vipin
PY - 2015
Y1 - 2015
N2 - 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 spatiotemporal 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 spatiotemporal 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.
AB - 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 spatiotemporal 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 spatiotemporal 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.
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M3 - Conference contribution
AN - SCOPUS:84949815953
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2575
EP - 2581
BT - IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
A2 - Wooldridge, Michael
A2 - Yang, Qiang
PB - International Joint Conferences on Artificial Intelligence
T2 - 24th International Joint Conference on Artificial Intelligence, IJCAI 2015
Y2 - 25 July 2015 through 31 July 2015
ER -