Advertisement

Prediction of next destinations from irregular patterns

  • Mehdi Boukhechba
  • Abdenour Bouzouane
  • Sébastien Gaboury
  • Charles Gouin-Vallerand
  • Sylvain Giroux
  • Bruno Bouchard
Original Research

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.

Keywords

Human activities Activity prediction Online association rules Concept drift 

References

  1. Asahara A, Maruyama K, Sato A, Seto K (2011) Pedestrian-movement prediction based on mixed Markov-chain model. ACM Press, Chicago, Illinois, p 25. doi: 10.1145/2093973.2093979 Google Scholar
  2. Boukhechba M, Bouzouane A, Bouchard B, Charles, GV, Sylvain G (2015) Online recognition of people’s activities from raw GPS data: semantic trajectory data analysis. In: Presented at the 8th ACM international conference on pervasive technologies related to assistive environmentsGoogle Scholar
  3. Boukhechba M, Bouzouane A, Bouchard B, Gouin-Vallerand C, Giroux S (2016) Energy optimization for outdoor activity recognition. J Sens 2016:1–15. doi: 10.1155/2016/6156914 CrossRefGoogle Scholar
  4. Ezeife CI, Su Y (2002) Mining incremental association rules with generalized FP-tree. In: Cohen R, Spencer B. (eds) Advances in artificial intelligence. Springer, Berlin, pp 147–160CrossRefGoogle Scholar
  5. Gai YL (2012) Research on data mining and apriori algorithm. Adv Mater Res 546–547:497–502. doi: 10.4028/www.scientific.net/AMR.546-547.497 CrossRefGoogle Scholar
  6. Gama J, Medas P, Castillo G, Rodrigues P (2004) Learning with drift detection. In: Bazzan A.L.C., Labidi S (eds) Advances in artificial intelligence—SBIA 2004. Springer, Berlin, pp 286–295CrossRefGoogle Scholar
  7. Gama J, Sebastião R, Rodrigues PP (2009) Issues in evaluation of stream learning algorithms. ACM Press. doi: 10.1145/1557019.1557060
  8. Gambs S, Killijian M-O, del Prado Cortez MN (2012) Next place prediction using mobility Markov chains. ACM Press, pp 1–6. doi: 10.1145/2181196.2181199
  9. Hipp J, Güntzer U, Nakhaeizadeh G (2000) Algorithms for association rule mining—a general survey and comparison. ACM SIGKDD Explor Newsl 2:58–64. doi: 10.1145/360402.360421 CrossRefGoogle Scholar
  10. Kapterev A (2014) Where to go next. In: Presentation secrets. Wiley, Hoboken, pp 265–278Google Scholar
  11. Katsaros D, Nanopoulos A, Karakaya M, Yavas G, Ulusoy Ö, Manolopoulos Y (2003) Clustering mobile trajectories for resource allocation in mobile environments. In: Berthold RM, Lenz H-J, Bradley E, Kruse R, Borgelt C (eds) Advances in intelligent data analysis V. Springer, Berlin, pp 319–329CrossRefGoogle Scholar
  12. Li K, Fu Y (2014) Prediction of human activity by discovering temporal sequence patterns. IEEE Trans Pattern Anal Mach Intell 36:1644–1657. doi: 10.1109/TPAMI.2013.2297321 CrossRefGoogle Scholar
  13. Morzy M (2006) Prediction of moving object location based on frequent trajectories. In: Levi A, Savaş E, Yenigün H, Balcısoy S, Saygın Y. (eds) Computer and information sciences—ISCIS. Springer, Berlin, pp 583–592Google Scholar
  14. PhridviRaj MSB, GuruRao CV (2014) Data mining—past, present and future—a typical survey on data streams. Proced Technol 12:255–263. doi: 10.1016/j.protcy.2013.12.483 CrossRefGoogle Scholar
  15. Simmons R, Browning B, Zhang Y, Sadekar V (2006) Learning to predict driver route and destination intent. IEEE, pp 127–132. doi: 10.1109/ITSC.2006.1706730
  16. Spaccapietra S, Parent C, Damiani ML, de Macedo JA, Porto F, Vangenot C (2008) A conceptual view on trajectories. Data Knowl Eng 65:126–146. doi: 10.1016/j.datak.2007.10.008 CrossRefGoogle Scholar
  17. Witten IH, Frank E, Hall MA (2017) Data mining: practical machine learning tools and techniques. Morgan Kaufman, 654 pGoogle Scholar
  18. Zhang X, Germain C, Sebag M, 2010. Adaptively detecting changes in Autonomic Grid Computing. IEEE, pp 387–392. doi: 10.1109/GRID.2010.5698017
  19. Zheng Y, Wang L, Zhang R, Xie X, Ma W-Y, 2008. GeoLife: managing and understanding your past life over maps. IEEE, pp. 211–212. doi: 10.1109/MDM.2008.20

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.LIARA Laboratory University of Quebec at Chicoutimi (UQAC)ChicoutimiCanada
  2. 2.Tele-universite of Quebec (TELUQ)MontrealCanada
  3. 3.University of SherbrookeSherbrookeCanada

Personalised recommendations