---
title: "Clustering Dynamic Spatio-Temporal Patterns in The Presence of Noise and Missing Data"
authors:
  - "Xi C. Chen"
  - "James H. Faghmous"
  - "Ankush Khandelwal"
  - "Vipin Kumar"
doi: ""
url: "https://ijcai.org/proceedings/2015/365"
pdf: "https://ijcai.org/proceedings/2015/0365.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "2575-2580"
track: "Special Track on Computational Sustainability"
keywords:
---

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

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

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, 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.