Clustering spatial data using random walks
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering objects on a spatial network
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Dual Clustering: Integrating Data Clustering over Optimization and Constraint Domains
IEEE Transactions on Knowledge and Data Engineering
Detection of emerging space-time clusters
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
SPANBRE: An Efficient Hierarchical Clustering Algorithm for Spatial Data with Neighborhood Relations
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Discovering correlated spatio-temporal changes in evolving graphs
Knowledge and Information Systems
IEEE Transactions on Knowledge and Data Engineering
Detecting and Tracking Spatio-temporal Clusters with Adaptive History Filtering
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Clustering Data Streams in Optimization and Geography Domains
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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This paper addresses spatio-temporal clustering of network data where the geometry and structure of the network is assumed to be static but heterogeneous due to the density of links varies cross the network. Road network, telecommunication network and internet are of these type networks. The thematic properties associated with the links of the network are dynamic, such as the flow, speed and journey time are varying in the peak and off-peak hours of a day. Analyzing the patterns of network data in space-time can help the understanding of the complexity of the networks Here a spatio-temporal clustering (STC) algorithm is developed to capture such dynamic patterns by fully exploiting the network characteristics in spatial, temporal and thematic domains. The proposed STC algorithm is tested on a part of London's traffic network to investigate how the clusters overlap on different days.