Adaptive K-Means for Clustering Air Mass Trajectories

Alex Mace, Roberto Sommariva, Zoe Fleming, Wenjia Wang

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2011
EditorsH Yin, Wenjia Wang, V Rayward-Smith
Number of pages8
Publication statusPublished - 2011
Event12th Internation Conference -
Duration: 1 Jan 2011 → …


Conference12th Internation Conference
Period1/01/11 → …

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