{"type":"ScholarlyArticle","title":"Clustering Dynamic Spatio-Temporal Patterns in The Presence of Noise and Missing Data","authors":[{"name":"Xi C. Chen","slug":"xi-c-chen","given":"Xi C.","family":"Chen","affiliation":""},{"name":"James H. Faghmous","slug":"james-h-faghmous","given":"James H.","family":"Faghmous","affiliation":""},{"name":"Ankush Khandelwal","slug":"ankush-khandelwal","given":"Ankush","family":"Khandelwal","affiliation":""},{"name":"Vipin Kumar","slug":"vipin-kumar-2","given":"Vipin","family":"Kumar","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2015/365","pdf":"https://ijcai.org/proceedings/2015/0365.pdf","markdown":"https://ijcai.org/proceedings/2015/365.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/365","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Computational Sustainability","track":"Special Track on Computational Sustainability","number":365,"pages":{"first":2575,"last":2580},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}