Algorithms
Maintaining knowledge about temporal intervals
Communications of the ACM
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
A survey of data mining and knowledge discovery software tools
ACM SIGKDD Explorations Newsletter
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Conceptualization of place via spatial clustering and co-occurrence analysis
Proceedings of the 2009 International Workshop on Location Based Social Networks
An ontology-based traffic accident risk mapping framework
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
| Hi-index | 0.00 |
NET-DBSCAN, a method for clustering the nodes of a linear network, whose edges may be temporarily inaccessible, is introduced. The new method extends the idea of a well-known spatial clustering method, named density-based spatial clustering of applications with noise (DBSCAN). The new algorithm is described in detail and through a series of examples. A prototype system, which implements the algorithm, developed in Java and tested through a series of synthetic networks, is also presented. Finally, the application of NET-DBSCAN method to support real-world situations is briefly discussed.