Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Clustering of streaming time series is meaningless
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Cellular automata and Hilditch thinning for extraction of user paths in online games
NetGames '06 Proceedings of 5th ACM SIGCOMM workshop on Network and system support for games
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Spatio-temporal discretization for sequential pattern mining
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Designing planned route based interactions for context-aware applications
Mobility '07 Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Modeling Herds and Their Evolvements from Trajectory Data
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
Towards a taxonomy of movement patterns
Information Visualization
Mining trajectory profiles for discovering user communities
Proceedings of the 2009 International Workshop on Location Based Social Networks
From trajectories to activities: a spatio-temporal join approach
Proceedings of the 2009 International Workshop on Location Based Social Networks
Trajectory pattern analysis for urban traffic
Proceedings of the Second International Workshop on Computational Transportation Science
Hiding co-occurring frequent itemsets
Proceedings of the 2009 EDBT/ICDT Workshops
Efficiently detecting clusters of mobile objects in the presence of dense noise
Proceedings of the 2010 ACM Symposium on Applied Computing
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
A system for destination and future route prediction based on trajectory mining
Pervasive and Mobile Computing
Discovering multi-label temporal patterns in sequence databases
Information Sciences: an International Journal
Regions of interest in trajectory data warehouse
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
A regression-based approach for mining user movement patterns from random sample data
Data & Knowledge Engineering
A personal route prediction system based on trajectory data mining
Information Sciences: an International Journal
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
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Discovering spatio-temporal causal interactions in traffic data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
How to use "classical" tree mining algorithms to find complex spatio-temporal patterns?
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
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
Social interaction mining in small group discussion using a smart meeting system
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Mining periodic behaviors of object movements for animal and biological sustainability studies
Data Mining and Knowledge Discovery
The pattern next door: towards spatio-sequential pattern discovery
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
International Journal of Intelligent Information and Database Systems
Understanding the Regularity and Variability of Human Mobility from Geo-trajectory
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Co-occurrence prediction in a large location-based social network
Frontiers of Computer Science: Selected Publications from Chinese Universities
Fast mining Top-Rank-k frequent patterns by using Node-lists
Expert Systems with Applications: An International Journal
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Many applications track the movement of mobile objects, which can be represented as sequences of timestamped locations. Given such a spatio-temporal series, we study the problem of discovering sequential patterns, which are routes frequently followed by the object. Sequential pattern mining algorithms for transaction data are not directly applicable for this setting. The challenges to address are (i) the fuzziness of locations in patterns, and (ii) the identification of non-explicit pattern instances. In this paper, we define pattern elements as spatial regions around frequent line segments. Our method first transforms the original sequence into a list of sequence segments, and detects frequent regions in a heuristic way. Then, we propose algorithms to find patterns by employing a newly proposed substring tree structure and improving Apriori technique. A performance evaluation demonstrates the effectiveness and efficiency of our approach.