Adaptive K-Means for Clustering Air Mass Trajectories

Mace, Alex, Sommariva, Roberto, Fleming, Zoe and Wang, Wenjia (2011) Adaptive K-Means for Clustering Air Mass Trajectories. In: Intelligent Data Engineering and Automated Learning - IDEAL 2011. Springer, pp. 1-8.

Full text not available from this repository. (Request a copy)

Abstract

Clustering air mass trajectories is used to identify source regions of certain chemical species. Current clustering methods only use the trajectory coordinates as clustering variables, and as such, are unable to differentiate between similar shaped trajectories that have different source regions and/or seasonal differences. This can lead to a higher variance in the chemical composition within each cluster and loss of information. We propose an adaptive K-means clustering algorithm that uses both the trajectory variables and the associated chemical value. We show, using carbon monoxide data from the Cape Verde for 2007, that our method produces a far more informative clustering than the existing standard method, whilst achieving a lower level of subjectivity.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
Depositing User:	Users 2731 not found.
Date Deposited:	03 Oct 2011 12:13
Last Modified:	22 Oct 2022 23:48
URI:	https://ueaeprints.uea.ac.uk/id/eprint/34912
DOI:	10.1007/978-3-642-23878-9_1

Actions (login required)

View Item