An Efficient Density Based Clustering Algorithm for Large Databases
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Network density estimation: analysis of point patterns over a network
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
A Query Language for Mobility Data Mining
International Journal of Data Warehousing and Mining
An Efficient Method for Discretizing Continuous Attributes
International Journal of Data Warehousing and Mining
Personalized behavior pattern recognition and unusual event detection for mobile users
Mobile Information Systems
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Rapid developments in the availability and access to spatially referenced information in a variety of areas have induced the need for better analytical techniques to understand the various phenomena. In particular, the authors' analysis is an insight into a wealth of geographical data collected by individuals as activity dairy data. The attention is drawn on point datasets corresponding to GPS traces driven along a same route in different days. In this paper, the authors explore the presence of clusters along the route, trying to understand the origins and motivations behind that to better understand the road network structure in terms of 'dense' spaces along the network. Therefore, the attention is focused on methods to highlight such clusters and see their impact on the network structure. Spatial clustering algorithms are examined DBSCAN and a comparison with other non-parametric density based algorithm Kernel Density Estimation is performed. Different tests are performed over the urban area of Trieste Italy, considering both multiple users and different origin/destination journeys.