The nature of statistical learning theory
The nature of statistical learning theory
Building Hierarchical Classifiers Using Class Proximity
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Application of the Self-Organizing Map to Trajectory Classification
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models
IEEE Transactions on Image Processing
A Framework for Trajectory Clustering
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
Trajectory Voting and Classification Based on Spatiotemporal Similarity in Moving Object Databases
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
On-line discovery of flock patterns in spatio-temporal data
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Trajectory Clustering via Effective Partitioning
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Efficient mutual nearest neighbor query processing for moving object trajectories
Information Sciences: an International Journal
Trajectory based behavior analysis for user verification
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Finding long and similar parts of trajectories
Computational Geometry: Theory and Applications
Fast and accurate trajectory streams clustering
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Non-separable transforms for clustering trajectories
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Median trajectories using well-visited regions and shortest paths
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A hybrid model and computing platform for spatio-semantic trajectories
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
GEOSO - a geo-social model: from real-world co-occurrences to social connections
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Similarity in (spatial, temporal and) spatio-temporal datasets
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
Trajectory analysis for user verification and recognition
Knowledge-Based Systems
NSS'12 Proceedings of the 6th international conference on Network and System Security
Privacy-preserving trajectory data publishing by local suppression
Information Sciences: an International Journal
Finding homogeneous groups in trajectory streams
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
Finding time period-based most frequent path in big trajectory data
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
QS-STT: QuadSection clustering and spatial-temporal trajectory model for location prediction
Distributed and Parallel Databases
Exploring pattern-aware travel routes for trajectory search
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
Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
Motif discovery in spatial trajectories using grammar inference
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Sequential pattern mining from trajectory data
Proceedings of the 17th International Database Engineering & Applications Symposium
Direction-preserving trajectory simplification
Proceedings of the VLDB Endowment
The influence of global constraints on similarity measures for time-series databases
Knowledge-Based Systems
Dealing with trajectory streams by clustering and mathematical transforms
Journal of Intelligent Information Systems
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Trajectory classification, i.e., model construction for predicting the class labels of moving objects based on their trajectories and other features, has many important, real-world applications. A number of methods have been reported in the literature, but due to using the shapes of whole trajectories for classification, they have limited classification capability when discriminative features appear at parts of trajectories or are not relevant to the shapes of trajectories. These situations are often observed in long trajectories spreading over large geographic areas. Since an essential task for effective classification is generating discriminative features, a feature generation framework TraClass for trajectory data is proposed in this paper, which generates a hierarchy of features by partitioning trajectories and exploring two types of clustering: (1) region-based and (2) trajectory-based. The former captures the higher-level region-based features without using movement patterns, whereas the latter captures the lower-level trajectory-based features using movement patterns. The proposed framework overcomes the limitations of the previous studies because trajectory partitioning makes discriminative parts of trajectories identifiable, and the two types of clustering collaborate to find features of both regions and sub-trajectories. Experimental results demonstrate that TraClass generates high-quality features and achieves high classification accuracy from real trajectory data.