Advertisement

Change Detection in Individual Users’ Behavior

  • Parisa RastinEmail author
  • Guénaël Cabanes
  • Basarab Matei
  • Jean-Marc Marty
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11140)

Abstract

The analysis of a dynamic data is challenging. Indeed, the structure of such data changes over time, potentially in a very fast speed. In addition, the objects in such data-sets are often complex. In this paper, our practical motivation is to perform users profiling, i.e. to follow users’ geographic location and navigation logs to detect changes in their habits and interests. We propose a new framework in which we first create, for each user, a signal of the evolution in the distribution of their interest and another signal based on the distribution of physical locations recorded during their navigation. Then, we detect automatically the changes in interest or locations thanks a new jump-detection algorithm. We compared the proposed approach with a set of existing signal-based algorithms on a set of artificial data-sets and we showed that our approach is faster and produce less errors for this kind of task. We then applied the proposed framework on a real data-set and we detected different categories of behavior among the users, from users with very stable interest and locations to users with clear changes in their behaviors, either in interest, location or both.

Keywords

Time series Change detection Signal-based approaches Users profiling 

References

  1. 1.
    Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases, VLDB 2003, VLDB Endowment, vol. 29, pp. 81–92. (2003)CrossRefGoogle Scholar
  2. 2.
    Arandiga, F., Cohen, A., Donat, R., Dyn, N., Matei, B.: Approximation of piecewise smooth functions and images by edge-adapted (ENO-EA) nonlinear multiresolution techniques. Appl. Comput. Harmonic Anal. 24(2), 225–250 (2008). Special Issue on Mathematical Imaging – Part IIMathSciNetCrossRefGoogle Scholar
  3. 3.
    Bifet, A.: Adaptive stream mining: pattern learning and mining from evolving data streams. In: Proceedings of the 2010 Conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams, pp. 1–212. IOS Press, Amsterdam (2010)Google Scholar
  4. 4.
    Cao, F., Estert, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise, pp. 328–339. Society for Industrial and Applied Mathematics (2006)Google Scholar
  5. 5.
    Chan, T.F., Zhou, H.M.: ENO-wavelet transforms for piecewise smooth functions. SIAM J. Numer. Anal. 40(4), 1369–1404 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chen, Y., Tu, L.: Density-based clustering for real-time stream data. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, pp. 133–142. ACM, New York (2007)Google Scholar
  7. 7.
    Claypoole, R.L., Davis, G.M., Sweldens, W., Baraniuk, R.G.: Nonlinear wavelet transforms for image coding via lifting. IEEE Trans. Image Process. 12(12), 1449–1459 (2003)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Dagan, I., Lee, L., Pereira, F.: Similarity-based methods for word sense disambiguation. In: Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics, EACL 1997, pp. 56–63 (1997). Association for Computational Linguistics, StroudsburgGoogle Scholar
  9. 9.
    Gama, J.: Knowledge Discovery from Data Streams, 1st edn. Chapman & Hall/CRC, Boca Raton (2010)CrossRefGoogle Scholar
  10. 10.
    Han, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco (2005)Google Scholar
  11. 11.
    Last, M.: Online classification of nonstationary data streams. Intell. Data Anal. 6(2), 129–147 (2002)zbMATHGoogle Scholar
  12. 12.
    Lipman, Y., Levin, D.: Approximating piecewise-smooth functions. IMA J. Numer. Anal. 30(4), 1159–1183 (2009)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Little, M.A., Jones, N.S.: Generalized methods and solvers for noise removal from piecewise constant signals, i. background theory. Proc. Roy. Soc. A 467(2135), 3088–3114 (2011)CrossRefGoogle Scholar
  14. 14.
    MacKay, D.J.C.: Information Theory, Inference and Learning Algorithms. Cambridge University Press, New York (2002)Google Scholar
  15. 15.
    Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  16. 16.
    Rastin, P., Matei, B.: Prototype-based clustering for relational data using Barycentric coordinates. In: Proceeding of the International Joint Conference on Neural Networks (IJCNN), IJCNN 2018 (2018)Google Scholar
  17. 17.
    Rastin, P., Zhang, T., Cabanes, G.: A new clustering algorithm for dynamic data. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9949, pp. 175–182. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46675-0_20CrossRefGoogle Scholar
  18. 18.
    Silva, J.A., Faria, E.R., Barros, R.C., Hruschka, E.R., de Carvalho, A.C., Gama, J.: Data stream clustering: a survey. ACM Comput. Surv. 46(1), 13–31 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Parisa Rastin
    • 1
    • 2
    Email author
  • Guénaël Cabanes
    • 1
    • 2
  • Basarab Matei
    • 1
    • 2
  • Jean-Marc Marty
    • 1
    • 2
  1. 1.LIPN-CNRS, UMR 7030, Université Paris 13VilletaneuseFrance
  2. 2.MindlytixParisFrance

Personalised recommendations