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.
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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
- Human activities
- Activity prediction
- Online association rules
- Concept drift