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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45(1), 1–34 (2012)
Ferreira, L.N., Zhao, L.: Time series clustering via community detection in networks. Inf. Sci. 326, 227–242 (2016)
Maharaj, E.A., D’Urso, P.: Fuzzy clustering of time series in the frequency domain. Inf. Sci. 181(7), 1187–1211 (2011)
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)
Deng, W., Wang, G., Xu, J.: Piecewise two-dimensional normal cloud representation for time-series data mining. Inf. Sci. 374(2016), 32–50 (2016)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)
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_4
Tang, L., Wang, X., Liu, H.: Community detection via heterogeneous interaction analysis. Data Min. Knowl. Discov. 25(1), 1–33 (2012)
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)
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_3
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)
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)
Zhang, Z., Li, Q., Zeng, D., Gao, H.: User community discovery from multi-relational networks. Decis. Support Syst. 54(2), 870–879 (2013)
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)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Wang, F., Li, T., Wang, X., Zhu, S., Ding, C.: Community discovery using nonnegative matrix factorization. Data Min. Knowl. Discov. 22, 493–521 (2010)
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)
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_10
https://archive.ics.uci.edu/ml/datasets/Robot+Execution+Failures
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17(2–3), 107–145 (2001)
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)
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)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Du, G., Zhou, L., Wang, L., Chen, H. (2018). Multivariate Time Series Clustering via Multi-relational Community Detection in Networks. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-96890-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96889-6
Online ISBN: 978-3-319-96890-2
eBook Packages: Computer ScienceComputer Science (R0)