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.
MAT-CA: a tool for Multiple Aspect Trajectory Clustering Analysis
Renso C;
2023-01-01
Abstract
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.File | Dimensione | Formato | |
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Descrizione: MAT-CA: a tool for Multiple Aspect Trajectory Clustering Analysis
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