Algorithms for clustering data
Algorithms for clustering data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Clustering objects on a spatial network
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A clustering algorithm based on maximal θ-distant subtrees
Pattern Recognition
Query and update efficient B+-tree based indexing of moving objects
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Effective density queries for moving objects in road networks
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Improved travel time prediction algorithms for intelligent transportation systems
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Dynamic k-means: a clustering technique for moving object trajectories
International Journal of Intelligent Information and Database Systems
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Spatial-Temporal clustering is one of the most important analysis tasks in spatial databases. Especially, in many real applications, real time data analysis such as clustering moving objects in spatial networks or traffic congestion prediction is more meaningful.Extensive method of clustering moving objects in Euclidean space is more complex and expensive. This paper proposes the scheme of clustering continuously moving objects, analyzes the fixed feature of the road network, proposes a notion of Virtual Clustering Unit (VCU) and improves on the existing algorithm. Performance analysis shows that the new scheme achieves high efficiency and accuracy for continuous clustering of moving objects in road networks.