Location-Aware Information Delivery with ComMotion
HUC '00 Proceedings of the 2nd international symposium on Handheld and Ubiquitous Computing
The Journal of Machine Learning Research
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
Extracting places from traces of locations
ACM SIGMOBILE Mobile Computing and Communications Review
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Activity-aware map: identifying human daily activity pattern using mobile phone data
HBU'10 Proceedings of the First international conference on Human behavior understanding
Discovering human places of interest from multimodal mobile phone data
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Mining Human Location-Routines Using a Multi-Level Approach to Topic Modeling
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
An Unsupervised Approach to Modeling Personalized Contexts of Mobile Users
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
GroupUs: Smartphone Proximity Data and Human Interaction Type Mining
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
Route classification using cellular handoff patterns
Proceedings of the 13th international conference on Ubiquitous computing
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model
ISWC '12 Proceedings of the 2012 16th Annual International Symposium on Wearable Computers (ISWC)
Scalable mining of common routes in mobile communication network traffic data
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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We present a new approach to address the problem of large sequence mining from big data. The particular problem of interest is the effective mining of long sequences from large-scale location data to be practical for Reality Mining applications, which suffer from large amounts of noise and lack of ground truth. To address this complex data, we propose an unsupervised probabilistic topic model called the distant n-gram topic model (DNTM). The DNTM is based on latent Dirichlet allocation (LDA), which is extended to integrate sequential information. We define the generative process for the model, derive the inference procedure, and evaluate our model on both synthetic data and real mobile phone data. We consider two different mobile phone datasets containing natural human mobility patterns obtained by location sensing, the first considering GPS/wi-fi locations and the second considering cell tower connections. The DNTM discovers meaningful topics on the synthetic data as well as the two mobile phone datasets. Finally, the DNTM is compared to LDA by considering log-likelihood performance on unseen data, showing the predictive power of the model. The results show that the DNTM consistently outperforms LDA as the sequence length increases.