Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Analyzing Relative Motion within Groups of Trackable Moving Point Objects
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient detection of motion patterns in spatio-temporal data sets
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Efficient mining of group patterns from user movement data
Data & Knowledge Engineering
Computing longest duration flocks in trajectory data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Dimensionality reduction for long duration and complex spatio-temporal queries
Proceedings of the 2007 ACM symposium on Applied computing
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Discovery of convoys in trajectory databases
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INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Convoy Queries in Spatio-Temporal Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
MoveMine: mining moving object databases
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
MoveMine: Mining moving object data for discovery of animal movement patterns
ACM Transactions on Intelligent Systems and Technology (TIST)
Mining significant time intervals for relationship detection
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Mining periodic behaviors of object movements for animal and biological sustainability studies
Data Mining and Knowledge Discovery
On the spatiotemporal burstiness of terms
Proceedings of the VLDB Endowment
Community trend outlier detection using soft temporal pattern mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Extracting trajectories through an efficient and unifying spatio-temporal pattern mining system
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Mining time relaxed gradual moving object clusters
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
GeT_move: an efficient and unifying spatio-temporal pattern mining algorithm for moving objects
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Mining multi-object spatial-temporal movement patterns
SIGSPATIAL Special
Online community detection in social sensing
Proceedings of the sixth ACM international conference on Web search and data mining
Map-based spatio-temporal interpolation in vehicle trajectory data using routing web-services
Proceedings of the 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science
Finding time period-based most frequent path in big trajectory data
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Calibrating trajectory data for similarity-based analysis
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Efficient event detection by exploiting crowds
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Efficient identification and approximation of k-nearest moving neighbors
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
WADS'13 Proceedings of the 13th international conference on Algorithms and Data Structures
A framework of traveling companion discovery on trajectory data streams
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Recent improvements in positioning technology make massive moving object data widely available. One important analysis is to find the moving objects that travel together. Existing methods put a strong constraint in defining moving object cluster, that they require the moving objects to stick together for consecutive timestamps. Our key observation is that the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Motivated by this, we propose the concept of swarm which captures the moving objects that move within arbitrary shape of clusters for certain timestamps that are possibly non-consecutive. The goal of our paper is to find all discriminative swarms, namely closed swarm. While the search space for closed swarms is prohibitively huge, we design a method, ObjectGrowth, to efficiently retrieve the answer. In ObjectGrowth, two effective pruning strategies are proposed to greatly reduce the search space and a novel closure checking rule is developed to report closed swarms on-the-fly. Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our methods.