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Multivariate Time Series Clustering via Multi-relational Community Detection in Networks

  • Guowang Du
  • Lihua ZhouEmail author
  • Lizhen Wang
  • Hongmei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10987)

Abstract

Clustering multivariate time series is a challenging problem with numerous applications. The presence of complex relations amongst individual series poses difficulties with respect to traditional modelling, computation and statistical theory. In this paper, we propose a method for clustering multivariate time series by using multi-relational community detection in complex networks. Firstly, a set of multivariate time series is transformed into a multi-relational network. Then, an algorithm for multi-relational community detection based on multiple nonnegative matrices factorization (MNMF) is proposed and is applied to identify time series clusters. The transformation of time series from time-space domain to topological domain benefits from the ability of networks to characterize both local and global relationship amongst nodes (representing data samples), while the use of MNMF can give full play to complex relations amongst individual series and preserve the multi-way nature of multivariate information. Preliminary experiment indicates promising results of our proposed approach.

Keywords

Multivariate time series Clustering Multi-relational network Community detection Matrix factorization 

Notes

Acknowledgement

This research was supported by the National Natural Science Foundation of China (61762090, 61262069, 61472346, and 61662086), The Natural Science Foundation of Yunnan Province (2016FA026, 2015FB114), the Project of Innovative Research Team of Yunnan Province, and Program for Innovation Research Team (in Science and Technology) in University of Yunnan Province (IRTSTYN).

References

  1. 1.
    Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45(1), 1–34 (2012)CrossRefGoogle Scholar
  2. 2.
    Ferreira, L.N., Zhao, L.: Time series clustering via community detection in networks. Inf. Sci. 326, 227–242 (2016)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Maharaj, E.A., D’Urso, P.: Fuzzy clustering of time series in the frequency domain. Inf. Sci. 181(7), 1187–1211 (2011)CrossRefGoogle Scholar
  4. 4.
    Huang, X.H., Ye, Y.M., Xiong, L.Y., Lau, R.Y.K., Jiang, N., Wang, S.K.: Time series k-means: a new k-means type smooth subspace clustering for time series data. Inf. Sci. 367–368(1), 1–13 (2016)Google Scholar
  5. 5.
    Deng, W., Wang, G., Xu, J.: Piecewise two-dimensional normal cloud representation for time-series data mining. Inf. Sci. 374(2016), 32–50 (2016)CrossRefGoogle Scholar
  6. 6.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wu, Z., Yin, W., Cao, J., Xu, G., Cuzzocrea, A.: Community detection in multi-relational social networks. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds.) WISE 2013. LNCS, vol. 8181, pp. 43–56. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41154-0_4CrossRefGoogle Scholar
  8. 8.
    Tang, L., Wang, X., Liu, H.: Community detection via heterogeneous interaction analysis. Data Min. Knowl. Discov. 25(1), 1–33 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Ströele, V., Zimbrão, G., Souza, J.M.: Group and link analysis of multi-relational scientific social networks. J. Syst. Softw. 86(7), 1819–1830 (2013)CrossRefGoogle Scholar
  10. 10.
    Zhou, L., Yang, P., Lü, K., Zhang, Z., Chen, H.: A coalition formation game theory-based approach for detecting communities in multi-relational networks. In: Dong, X., Yu, X., Li, J., Sun, Y. (eds.) WAIM 2015. LNCS, vol. 9098, pp. 30–41. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-21042-1_3CrossRefGoogle Scholar
  11. 11.
    Saad, W., Han, Z., Debbah, M., Hjørungnes, A., Basar, T.: Coalitional game theory for communication networks: a tutorial. IEEE Signal Process. Mag. 26(5), 77–97 (2009)CrossRefGoogle Scholar
  12. 12.
    Lin, Y.-R., Choudhury, M.D., Sundaram, H., Kelliher, A.: Discovering multi-relational structure in social media streams. ACM Trans. Multimed. Comput. Commun. Appl. 8(1), 1–28 (2012)CrossRefGoogle Scholar
  13. 13.
    Zhang, Z., Li, Q., Zeng, D., Gao, H.: User community discovery from multi-relational networks. Decis. Support Syst. 54(2), 870–879 (2013)CrossRefGoogle Scholar
  14. 14.
    Li, X.T., Ng, M.K., Ye, Y.M.: Multicomm: finding community structure in multi-dimensional networks. IEEE Trans. Knowl. Data Eng. 26(4), 929–941 (2014)CrossRefGoogle Scholar
  15. 15.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRefGoogle Scholar
  16. 16.
    Wang, F., Li, T., Wang, X., Zhu, S., Ding, C.: Community discovery using nonnegative matrix factorization. Data Min. Knowl. Discov. 22, 493–521 (2010)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Ma, X., Dong, D.: Evolutionary nonnegative matrix factorization algorithms for community detection in dynamic networks. IEEE Trans. Knowl. Data Eng. 29(5), 1045–1058 (2017)CrossRefGoogle Scholar
  18. 18.
    Gupta, S.K., Phung, D., Adams, B., Venkatesh, S.: A matrix factorization framework for jointly analyzing multiple nonnegative data sources. In: Yada, K. (ed.) Data Mining for Service. SBD, vol. 3, pp. 151–170. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-642-45252-9_10CrossRefGoogle Scholar
  19. 19.
  20. 20.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17(2–3), 107–145 (2001)CrossRefGoogle Scholar
  21. 21.
    Huang, X., Ye, Y., Guo, H., Cai, Y., Zhang, H., Li, Y.: DSKmeans: a new kmeans-type approach to discriminative subspace clustering. Knowl. Based Syst. 70(2014), 293–300 (2014)CrossRefGoogle Scholar
  22. 22.
    Solomonoff, A., Mielke, A., Schmidt, M., Gish, H.: Clustering speakers by their voices. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 757–760 (1998)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Guowang Du
    • 1
  • Lihua Zhou
    • 1
    Email author
  • Lizhen Wang
    • 1
  • Hongmei Chen
    • 1
  1. 1.School of InformationYunnan UniversityKunmingChina

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