Document - MAT-CA: a tool for Multiple Aspect Trajectory Clustering Analysis

2023

Conference article Open Access

MAT-CA: a tool for Multiple Aspect Trajectory Clustering Analysis

Santos Y., Giuliani R., Portela T., Renso C., Carvalho J.

Multiple aspects trajectories Semantic trajectories Clustering

Multiple aspect trajectory (MAT) is a relevant concept that enables mining interesting patterns moving objects for di!erent applications. This new way of looking at trajectories includes a semantic dimension, which presents the notion of aspects that are relevant facts of the real world that add more meaning to spatio-temporal data. The high dimensionality and heterogeneity of these data makes clustering a very challenging task both in terms of e"ciency and quality. The present demo o!ers a tool, called MAT-CA, to support the user in the clustering task of MATs, speci#cally for identifying and visualizing the hidden patterns. The MAT-CA join into the same tool a multiple aspects trajectories clustering method and visual analysis of the results. We illustrate the use of the tool for o!ering both clustering output visualization and statistics.

Source: EMODE '23: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Methods for Enriched Mobility Data: Emerging issues and Ethical perspectives, pp. 40–43, Hamburg, Germany, 13/11/2023

Metrics

Back to previous page

Cite as

BibTeX entry

@inproceedings{oai:it.cnr:prodotti:489973,
	title = {MAT-CA: a tool for Multiple Aspect Trajectory Clustering Analysis},
	author = {Santos Y. and Giuliani R. and Portela T. and Renso C. and Carvalho J.},
	doi = {10.1145/3615885.3628009},
	booktitle = {EMODE '23: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Methods for Enriched Mobility Data: Emerging issues and Ethical perspectives, pp. 40–43, Hamburg, Germany, 13/11/2023},
	year = {2023}
}