Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Discovering personally meaningful places: An interactive clustering approach
ACM Transactions on Information Systems (TOIS)
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
SerPens: a tool for semantically enriched location information on personal devices
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Understanding transportation modes based on GPS data for web applications
ACM Transactions on the Web (TWEB)
Find me if you can: improving geographical prediction with social and spatial proximity
Proceedings of the 19th international conference on World wide web
Mining periodic behaviors for moving objects
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining significant semantic locations from GPS data
Proceedings of the VLDB Endowment
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing traditional local search recommendations with context-awareness
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
NextPlace: a spatio-temporal prediction framework for pervasive systems
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Toward traffic-driven location-based web search
Proceedings of the 20th ACM international conference on Information and knowledge management
Subject-oriented top-k hot region queries in spatial dataset
Proceedings of the 20th ACM international conference on Information and knowledge management
Finding Regions of Interest from Trajectory Data
MDM '11 Proceedings of the 2011 IEEE 12th International Conference on Mobile Data Management - Volume 01
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Recent research on geographical data mining that focuses on user behavior is lacking some fundamental aspects, measurements rely on large quantities of geographic data and lack contextual information. This work introduces a novel knowledge discovery architecture that brings together machine learning techniques with readily available information from popular Location Social Networks, in order to enrich geographical locations with context and add meaning to user behavior. Results show that through analysis of context enriched data we are capable of inferring context for detected user points of interest and patterns, such as where the user lives, works and spends his free time, without a large quantity of information or prior knowledge of the user and his private data.