A general methodology for n-dimensional trajectory clustering

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. (C) 2015 Elsevier Ltd. All rights reserved.