Abstract
In this paper we propose a demo of a Visual Analytics Toolkit to cope with the complexity of analysing a large dataset of moving objects, in a step wise manner. We allow the user to sample a small subset of objects, that can be handled in main memory, and to perform the analysis on this small group by means of a density based clustering algorithm. The GUI is designed in order to exploit and facilitate the human interaction during this phase of the analysis, to select interesting clusters among the candidates. The selected groups are used to build a classifier that can be used to label other objects from the original dataset. The classifier can then be used to efficiently associate all objects in the database to clusters. The tool has been tested using a large set of GPS tracked cars.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., Andrienko, G.: Visually driven analysis of movement data by progressive clustering. Information Visualization 7(3-4), 225–239 (2008)
Pelekis, N., Kopanakis, I., Marketos, G., Ntoutsi, I., Andrienko, G.L., Theodoridis, Y.: Similarity search in trajectory databases. In: TIME, pp. 129–140 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Andrienko, G., Andrienko, N., Rinzivillo, S., Nanni, M., Pedreschi, D. (2009). A Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data. In: Mamoulis, N., Seidl, T., Pedersen, T.B., Torp, K., Assent, I. (eds) Advances in Spatial and Temporal Databases. SSTD 2009. Lecture Notes in Computer Science, vol 5644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02982-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-02982-0_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02981-3
Online ISBN: 978-3-642-02982-0
eBook Packages: Computer ScienceComputer Science (R0)