Algorithms for clustering data
Algorithms for clustering data
Information Theoretic Clustering of Sparse Co-Occurrence Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Characterizing flows in large wireless data networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Characterizing mobility and network usage in a corporate wireless local-area network
Proceedings of the 1st international conference on Mobile systems, applications and services
CRAWDAD: A Community Resource for Archiving Wireless Data at Dartmouth
IEEE Pervasive Computing
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Model T++: an empirical joint space-time registration model
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Analysis of a campus-wide wireless network
Wireless Networks
Periodic properties of user mobility and access-point popularity
Personal and Ubiquitous Computing
Mining behavioral groups in large wireless LANs
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Profile-cast: behavior-aware mobile networking
ACM SIGMOBILE Mobile Computing and Communications Review
The changing usage of a mature campus-wide wireless network
Computer Networks: The International Journal of Computer and Telecommunications Networking
Modeling spatial and temporal dependencies of user mobility in wireless mobile networks
IEEE/ACM Transactions on Networking (TON)
Data-driven co-clustering model of internet usage in large mobile societies
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
Identify the User's Information Need Using the Current Search Context
International Journal of Enterprise Information Systems
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User online behavior and interests will play a central role in future mobile networks. We introduce a systematic method for large-scale multi-dimensional modeling and analysis of online activity and mobility for thousands of mobile users across 79 buildings over a variety of web domains. We propose a modeling approach based on kind of neural-networks, called self-organizing maps (SOM), for discovering, organizing and visualizing different mobile users' trends from billions of WLAN records. We find surprisingly that users' trends based on domains and locations can be accurately modeled using a self-organizing map with clearly distinct characteristics. We also find many non-trivial correlations between different types of web domains and locations.