Abstract
Current advances in location tracking technology provide exceptional amount of data about the users’ movements. The volume of geospatial data collected from moving users’ challenges human ability to analyze the stream of input data. Therefore, new methods for online mining of moving object data are required. One of the popular approaches available for moving objects is the prediction of the unknown future location of an object. In this paper we present a new method for online prediction of users’ next important locations to be visited that not only learns incrementally the users’ habits, but also detects and supports the drifts in their patterns. Our original contribution includes a new algorithm of online mining association rules that support the concept drift.
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- 1.
Apriori is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
- 2.
MMC is a probabilistic automaton in which states represent points of interest (POIs) of an individual and transitions between states corresponds to a movement from one POI to another one, a transition between POIs is non deterministic but rather that there is a probability distribution over the transitions that corresponds to the probability of moving from one POI to another.
- 3.
MMM is an intermediate model between individual and generic models. The prediction of the next location is based on a Markov model belonging to a group of individuals with similar mobility behavior. This approach clusters individuals into groups based on their mobility traces and then generates a specific Markov model for each group. The prediction of the next location works by first identifying the group a particular individual belongs to and then inferring the next location based on this group model.
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Boukhechba, M., Bouzouane, A., Bouchard, B., Gouin-Vallerand, C., Giroux, S. (2015). Online Prediction of People’s Next Point-of-Interest: Concept Drift Support. In: Salah, A., Kröse, B., Cook, D. (eds) Human Behavior Understanding. Lecture Notes in Computer Science(), vol 9277. Springer, Cham. https://doi.org/10.1007/978-3-319-24195-1_8
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