SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 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
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Distance Measures for Effective Clustering of ARIMA Time-Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Using sets of feature vectors for similarity search on voxelized CAD objects
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Translation-invariant mixture models for curve clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On detecting space-time clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the tenth 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
Mining mobile group patterns: a trajectory-based approach
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Analyzing Trajectories Using Uncertainty and Background Information
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Visually driven analysis of movement data by progressive clustering
Information Visualization
Taxonomy-driven lumping for sequence mining
Data Mining and Knowledge Discovery
Finding long and similar parts of trajectories
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Proceedings of the 5th French-Speaking Conference on Mobility and Ubiquity Computing
Efficiently detecting clusters of mobile objects in the presence of dense noise
Proceedings of the 2010 ACM Symposium on Applied Computing
Towards the next generation of location-based services
W2GIS'07 Proceedings of the 7th international conference on Web and wireless geographical information systems
Discovering private trajectories using background information
Data & Knowledge Engineering
Movement Data Anonymity through Generalization
Transactions on Data Privacy
Clustering vessel trajectories with alignment kernels under trajectory compression
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Unsupervised trajectory sampling
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Event-based semantic visualization of trajectory data in urban city with a space-time cube
VIS '10 Proceedings of the 3rd WSEAS international conference on Visualization, imaging and simulation
Finding long and similar parts of trajectories
Computational Geometry: Theory and Applications
Trajectory anonymity in publishing personal mobility data
ACM SIGKDD Explorations Newsletter
Mining pixel evolutions in satellite image time series for agricultural monitoring
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Mining spatial trajectories using non-parametric density functions
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Online and offline trend cluster discovery in spatially distributed data streams
MSM'10/MUSE'10 Proceedings of the 2010 international conference on Analysis of social media and ubiquitous data
Unveiling the complexity of human mobility by querying and mining massive trajectory data
The VLDB Journal — The International Journal on Very Large Data Bases
Deriving implicit indoor scene structure with path analysis
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
Median trajectories using well-visited regions and shortest paths
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Mining trajectory corridors using fréchet distance and meshing grids
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Visually exploring movement data via similarity-based analysis
Journal of Intelligent Information Systems
Similarity in (spatial, temporal and) spatio-temporal datasets
Proceedings of the 15th International Conference on Extending Database Technology
Proceedings of the 15th International Conference on Extending Database Technology
Machine learning for vessel trajectories using compression, alignments and domain knowledge
Expert Systems with Applications: An International Journal
A spatial clustering method for points-with-directions
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Dynamic k-means: a clustering technique for moving object trajectories
International Journal of Intelligent Information and Database Systems
A GPU approach to subtrajectory clustering using the Fréchet distance
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
International Journal of Intelligent Information and Database Systems
A Query Language for Mobility Data Mining
International Journal of Data Warehousing and Mining
Graph-Based approaches to clustering network-constrained trajectory data
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Multi-level association rules and directed graphs for spatial data analysis
Expert Systems with Applications: An International Journal
TODMIS: mining communities from trajectories
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Algorithms for hotspot computation on trajectory data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A Hybrid Grid-based Method for Mining Arbitrary Regions-of-Interest from Trajectories
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
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Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future, due to both technological and social/commercial reasons. From the data mining viewpoint, spatio-temporal trajectory data introduce new dimensions and, correspondingly, novel issues in performing the analysis tasks. In this paper, we consider the clustering problem applied to the trajectory data domain. In particular, we propose an adaptation of a density-based clustering algorithm to trajectory data based on a simple notion of distance between trajectories. Then, a set of experiments on synthesized data is performed in order to test the algorithm and to compare it with other standard clustering approaches. Finally, a new approach to the trajectory clustering problem, called temporal focussing, is sketched, having the aim of exploiting the intrinsic semantics of the temporal dimension to improve the quality of trajectory clustering.