A general methodology for n-dimensional trajectory clustering

Bermingham, Luke, and Lee, Ickjai (2015) A general methodology for n-dimensional trajectory clustering. Expert Systems with Applications, 42 (21). pp. 7573-7581.

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Trajectory data is rich in dimensionality, often containing valuable patterns in more than just the spatial and temporal dimensions. Yet existing trajectory clustering techniques only consider a fixed number of dimensions. We propose a general trajectory clustering methodology which can detect clusters using any arbitrary number of the n-dimensions available in the data. To exemplify our methodology we apply it an existing trajectory clustering approach, TRACLUS, to create the so-called, ND-TRACLUS. Furthermore, in order to better describe the trajectory clusters uncovered when clustering arbitrary dimensions we also introduce, Retraspam, a novel algorithm for n-dimensional representative trajectory formulation. We qualitatively and quantitatively evaluate both our methodology and Retraspam using two real world datasets and find valuable, previously unknown higher dimensional trajectory patterns.

Item ID: 42508
Item Type: Article (Research - C1)
ISSN: 1873-6793
Keywords: trajectory clustering; high dimensional clustering; trajectory data mining
Date Deposited: 12 Feb 2016 05:05
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890201 Application Software Packages (excl. Computer Games) @ 100%
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