The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Exploring group mobility for replica data allocation in a mobile environment
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
AGILE: A General Approach to Detect Transitions in Evolving Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
On Mining Moving Patterns for Object Tracking Sensor Networks
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
CarWeb: A Traffic Data Collection Platform
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Exploring group moving pattern for an energy-constrained object tracking sensor network
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Finding similar users using category-based location history
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
A framework of mining semantic regions from trajectories
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Storing routes in socio-spatial networks and supporting social-based route recommendation
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Circle of friend query in geo-social networks
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
QS-STT: QuadSection clustering and spatial-temporal trajectory model for location prediction
Distributed and Parallel Databases
Efficient location aware intrusion detection to protect mobile devices
Personal and Ubiquitous Computing
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With the rapid development of positioning techniques (e.g., GPS), users can easily collect their trajectories. Furthermore, with the growing of Web 2.0, some web sites allow users to share their own trajectories. In such web sites, users are able to search trajectories that are interested by users. To provide more insights into these trajectories, in this paper, we target at the problem of discovering communities among users, where users in the same community have similar moving behaviors. Note that moving behaviors are usually represented as trajectory patterns where a user frequently travels. In this paper, we propose a framework to discover communities of users. Explicitly, we adopt a probabilistic suffix tree (abbreviated as PST) as a trajectory profile which truly reflects user moving behavior of a user. In light of trajectory profiles, we further formulate a similarity measurement among trajectory profiles of users. Based on the similarity measurement, we develop algorithm CI (standing for Community Identification) to discover user communities. Furthermore, for the same community, one representative PST is selected. When a new user is added, one could simply derive the similarity measurement by comparing representative PSTs, which is able to efficiently determine which community this new user should join. To evaluate our proposed methods, we conduct experiments on the synthetic dataset generated from one real dataset. Experimental results show that the trajectory profile proposed can effectively reflect user moving behavior, and our proposed methods can accurately identify communities among users.