Abstract
Few decades ago, understanding human behaviors was considered as a mystery where predicting people’s future was impossible. Many changes have been noticed since that era. Thanks to current advances in location tracking technology and data mining techniques, predicting users’ behaviors has become possible. In this paper we present a new algorithm to online predict users’ next visited locations 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 for online association rules mining that supports the concept drift.
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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.
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.
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., Gaboury, S. et al. Prediction of next destinations from irregular patterns. J Ambient Intell Human Comput 9, 1345–1357 (2018). https://doi.org/10.1007/s12652-017-0519-z
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DOI: https://doi.org/10.1007/s12652-017-0519-z