Authors: Gennady Andrienko, Natalia Andrienko, Georg Fuchs, Jose Manuel Cordero Garcia
Abstract: Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance \ functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may \ include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, \ i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories \ by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may \ become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of \ trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for \ further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from \ the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by \ means of relevance-aware trajectory clustering.