Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
URBAN CRIME ANALYSIS THROUGH AREAL CATEGORIZED MULTIVARIATE ASSOCIATIONS MINING
Applied Artificial Intelligence
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
An empirical study on mining sequential patterns in a grid computing environment
Expert Systems with Applications: An International Journal
Mining Travel Patterns from Geotagged Photos
ACM Transactions on Intelligent Systems and Technology (TIST)
Expert Systems with Applications: An International Journal
Points-of-Interest Mining from People's Photo-Taking Behavior
HICSS '13 Proceedings of the 2013 46th Hawaii International Conference on System Sciences
Mining Points-of-Interest Association Rules from Geo-tagged Photos
HICSS '13 Proceedings of the 2013 46th Hawaii International Conference on System Sciences
Exploration of geo-tagged photos through data mining approaches
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Geo-tagged photos leave trails of movement that form trajectories. Regions-of-interest detection identifies interesting hot spots where many trajectories visit and large geo-tagged photos are uploaded. Extraction of exact shapes of regions-of-interest is a key step to understanding these trajectories and mining sequential trajectory patterns. This article introduces an efficient and effective grid-based regions-of-interest detection method that is linear to the number of grid cells, and is able to detect arbitrary shapes of regions-of-interest. The proposed algorithm is combined with sequential pattern mining to reveal sequential trajectory patterns. Experimental results reveal quality regions-of-interest and promising sequential trajectory patterns that demonstrate the benefits of our algorithm.