Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Trajectory queries and octagons in moving object databases
Proceedings of the eleventh international conference on Information and knowledge management
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Coarse-to-Fine Strategy for Vehicle Motion Trajectory Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Time-focused clustering of trajectories of moving objects
Journal of Intelligent Information Systems
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A conceptual view on trajectories
Data & Knowledge Engineering
Trajectory retrieval with latent semantic analysis
Proceedings of the 2008 ACM symposium on Applied computing
The anatomy of a large-scale social search engine
Proceedings of the 19th international conference on World wide web
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Towards mobility-based clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
SHRINK: a structural clustering algorithm for detecting hierarchical communities in networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Top-Eye: top-k evolving trajectory outlier detection
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Clustering uncertain trajectories
Knowledge and Information Systems
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
International Journal of Computer Vision
Unveiling the complexity of human mobility by querying and mining massive trajectory data
The VLDB Journal — The International Journal on Very Large Data Bases
On selection of objective functions in multi-objective community detection
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Community detection in incomplete information networks
Proceedings of the 21st international conference on World Wide Web
On Discovery of Traveling Companions from Streaming Trajectories
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Multifeature Object Trajectory Clustering for Video Analysis
IEEE Transactions on Circuits and Systems for Video Technology
Semantic trajectories: Mobility data computation and annotation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Fast Detection of Dense Subgraphs with Iterative Shrinking and Expansion
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Existing algorithms for trajectory-based clustering usually rely on simplex representation and a single proximity-related distance (or similarity) measure. Consequently, additional information markers (e.g., social interactions or the semantics of the spatial layout) are usually ignored, leading to the inability to fully discover the communities in the trajectory database. This is especially true for human-generated trajectories, where additional fine-grained markers (e.g., movement velocity at certain locations, or the sequence of semantic spaces visited) can help capture latent relationships between cluster members. To address this limitation, we propose TODMIS: a general framework for Trajectory cOmmunity Discovery using Multiple Information Sources. TODMIS combines additional information with raw trajectory data and creates multiple similarity metrics. In our proposed approach, we first develop a novel approach for computing semantic level similarity by constructing a Markov Random Walk model from the semantically-labeled trajectory data, and then measuring similarity at the distribution level. In addition, we also extract and compute pair-wise similarity measures related to three additional markers, namely trajectory level spatial alignment (proximity), temporal patterns and multi-scale velocity statistics. Finally, after creating a single similarity metric from the weighted combination of these multiple measures, we apply dense sub-graph detection to discover the set of distinct communities. We evaluated TODMIS extensively using traces of (i) student movement data in a campus, (ii) customer trajectories in a shopping mall, and (iii) city-scale taxi movement data. Experimental results demonstrate that TODMIS correctly and efficiently discovers the real grouping behaviors in these diverse settings.