Algorithms for clustering data
Algorithms for clustering data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of 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
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
C2P: Clustering based on Closest Pairs
Proceedings of the 27th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd 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
Clustering moving objects for spatio-temporal selectivity estimation
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Mining outliers in spatial networks
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Efficiently detecting clusters of mobile objects in the presence of dense noise
Proceedings of the 2010 ACM Symposium on Applied Computing
Proceedings of the 13th international conference on Ubiquitous computing
A moving zone based architecture for message dissemination in VANETs
Proceedings of the 8th International Conference on Network and Service Management
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Advances in wireless networks and positioning technologies (e.g., GPS) have enabled new data management applications that monitor moving objects. In such new applications, realtime data analysis such as clustering analysis is becoming one of the most important requirements. In this paper, we present the problem of clustering moving objects in spatial networks and propose a unified framework to address this problem. Due to the innate feature of continuously changing positions of moving objects, the clustering results dynamically change. By exploiting the unique features of road networks, our framework first introduces a notion of cluster block (CB) as the underlying clustering unit. We then divide the clustering process into the continuous maintenance of CBs and periodical construction of clusters with different criteria based on CBs. The algorithms for efficiently maintaining and organizing the CBs to construct clusters are proposed. Extensive experimental results show that our clustering framework achieves high efficiency for clustering moving objects in real road networks.