BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
On-line discovery of flock patterns in spatio-temporal data
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Traffic density-based discovery of hot routes in road networks
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
MARiO: multi-attribute routing in open street map
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
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Various routing algorithms compute sets of alternative routes to allow users to select the route appearing to be most attractive. A problem with the result set of this type of solution is that the number of retrieved routes might exceed the number of choices being manageable by a user. In this paper, we address the problem of selecting small sets of routes which still represent the general alternatives. To decide which routes to prune, we employ an error bound on the total cost of two alternatives. Since two routes having approximately the same cost might visit disjunctive parts of the network, pure cost-based pruning might discard important choices. To prevent loosing these alternatives, we define a second criterion based on local invariance. In our experimental setting, we examine run times and compression rates for the case of route skylines in Open Street Map data.