Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
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
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The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular our analysis represents a first 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. Our aim here is to explore the presence of clusters along the route, trying to understand the origins and motivations behind that in order to better understand the road network structure in terms of 'dense' spaces along the network. In this paper the attention is therefore 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. A test is performed over the urban area of Trieste (Italy).