Probability, random processes, and estimation theory for engineers
Probability, random processes, and estimation theory for engineers
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
ACM Computing Surveys (CSUR)
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Mixtures of ARMA Models for Model-Based Time Series Clustering
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Time-focused clustering of trajectories of moving objects
Journal of Intelligent Information Systems
Mixed-Drove Spatio-Temporal Co-occurence Pattern Mining: A Summary of Results
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets
IEEE Transactions on Knowledge and Data Engineering
On-line discovery of hot motion paths
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Computational Geometry: Theory and Applications
Continuous Clustering of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Zonal Co-location Pattern Discovery with Dynamic Parameters
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Continuous k-Means Monitoring over Moving Objects
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
Balancing Spectral Clustering for Segmenting Spatio-temporal Observations of Multi-agent Systems
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Clustering moving objects in spatial networks
DASFAA'07 Proceedings of the 12th 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
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We address the problem of detecting and tracking clusters of moving objects in very noisy environments. Monitoring a crowded football stadium for small groups of individuals acting suspiciously is an example of one such environment. In this scenario the vast majority of individuals are not part of suspicious groups and are considered noise. Existing spatio-temporal clustering algorithms are either incapable of detecting small clusters in extreme noise, or have high computation and storage requirements that prohibit their use in real-time alerting systems. We propose a technique called Dynamic Density Based Clustering (DDBC) that utilizes the relational history of the moving objects to increase the accuracy of the clustering algorithm. The incorporation of this information into the clustering algorithm is done efficiently and implicitly by using a relationship graph. The relationship graph incrementally estimates the strength of the relationships between moving objects. A modified DBSCAN algorithm is then used to find clusters of highly related objects from the relationship graph. We evaluate the DDBC technique experimentally on a number of data sets of mobile objects. The experiments show that DDBC outperforms both Trajectory Mining and Moving Cluster Mining techniques in terms of accuracy, memory usage, and computation time as the density of noise increases.