Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Least squares quantization in PCM
IEEE Transactions on Information Theory
Predicting location using mobile phone calls
ACM SIGCOMM Computer Communication Review - Special october issue SIGCOMM '12
When and where next: individual mobility prediction
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
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Predicting the location of people from their mobile phone logs is becoming an attractive research area. Due to two main reasons this problem is very challenging: the log data is very large and there is a variety of granularity levels both for specifying the location and the time, especially with low granularity level it becomes much more complicated to define common user behaviour patterns. In this work, rather than determining the next location of a person, we focus on the predicting the location of a person when it changes. We employed a two phase method; which first clusters the data to obtain a higher granularity level, and then extracts frequent sequential patterns corresponding to location changes. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results in predicting the change of location of mobile phone users.